Category: building student engagement

Reviving Engagement in the Spanish Classroom: A Musical Challenge with ChatGPT – Faculty Focus
Halfway through the semester, it’s not uncommon to notice a shift in your students’ energy levels (Baghurst and Kelley, 2013; Kumari et al., 2021). The initial enthusiasm for learning a foreign language may wane as other courses with demanding assignments compete for their attention. Some students prioritize subjects they perceive as more directly tied to their major or career, while others simply feel the weight of mid-semester exhaustion. In the spring, the long winter months can add to this fatigue, making it even harder to keep students engaged (Rohan and Sigmon, 2000).

This is the moment when a language instructor must pivot, shifting the classroom dynamic to reignite curiosity and motivation. Although instructors strive to incorporate activities that cater to the five preferred learning styles (Felder and Henriques, 1995)—visual (learning through images and spatial understanding), auditory (learning through listening and discussion), reading/writing (learning through text-based interaction), kinesthetic (learning through movement and hands-on activities), and multimodal (a combination of multiple styles)—it is beneficial to intersperse these well-structured, and, after a while, predictable classes with activities that break the mold. Introducing something unexpected and different from the established classroom dynamics can reenergize students, foster creativity, and enhance their enthusiasm for learning.

Music, in particular, has long been an ally of instructors teaching a second language (L2)—a language learned after one’s native tongue—especially since the field transitioned toward a more communicative approach. Rooted in interaction and real-world application, the communicative approach prioritizes meaningful engagement over rote memorization, helping students develop fluency in natural, immersive ways. Research has consistently highlighted the benefits of music in L2 acquisition, from improving pronunciation and listening skills to enhancing vocabulary retention and cultural understanding (Degrave, 2019; Kumar et al. 2022; Nuessel and Marshall, 2008; Vidal and Nordgren, 2024).

Building on this tradition, the activity we will share here not only incorporates music but also integrates artificial intelligence, adding a new layer of engagement and critical thinking. By using AI as a tool in the learning process, students not only familiarize themselves with its capabilities but also develop the ability to critically evaluate the content it generates. This approach encourages them to reflect on language, meaning, and interpretation while engaging in text analysis, creative writing, public speaking, and gamification—all within an interactive, culturally rich framework.

Activity Description: Musical Challenge with ChatGPT: “Sing and Discover”

Objective:

Students will improve their listening comprehension and written production in Spanish by analyzing and recreating song lyrics with the help of ChatGPT. While the instructions are presented here in English, the activity should be conducted in the target language—whether Spanish or another language being taught.

Instructions:

1. Listen and Decode
- Divide the class into groups of 2-3 students.
- Choose a song in Spanish (e.g., La Llorona by Chavela Vargas, Oye Cómo Va by Tito Puente, Vivir Mi Vida by Marc Anthony).
- Provide each group with an incomplete version of the lyrics with missing words.
- Students listen to the song and fill in the blanks.
2. Interpret and Discuss
- Within their groups, students analyze the song’s meaning.
- They discuss what they think the lyrics convey, including emotions, themes, and any cultural references they recognize.
  
  Each group shares their interpretation with the class.
  
  What do you think the song is trying to communicate?
  
  Which emotions or feelings do the lyrics evoke for you?
  
  Can you identify any cultural references in the song? How do they shape its meaning?
  
  How does the music (melody, rhythm, etc.) influence your interpretation of the lyrics?
3. Compare with ChatGPT
- After forming their own analysis, students ask ChatGPT:
  
  What do you think the song is trying to communicate?
  
  Which emotions or feelings do the lyrics evoke for you?
- They compare ChatGPT’s interpretation with their own ideas and discuss similarities or differences.
4. Create Your Own Verse
- Each group writes a new verse that matches the song’s style and rhythm.
- They may ask ChatGPT for assistance: “Help us write a new verse for this song in the same style.”
5. Perform and Sing
- Each group presents their new verse to the class.
- If they feel comfortable, they can sing it using the original melody.
- It is beneficial for the professor to have a karaoke (instrumental) version of the song available so that students’ lyrics can be heard clearly.
- Displaying the new lyrics on a monitor or projector allows other students to follow along and sing together, enhancing the collective experience.
6. Election – The Grammy Goes To
Students vote for different categories, including:
- Best Adaptation
- Best Reflection
- Best Performance
- Best Attitude
- Best Collaboration
7. Final Reflection
- What was the most challenging part of understanding the lyrics?
- How did ChatGPT help interpret the song?
- What new words or expressions did you learn?
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June 6, 2025
How to Significantly Improve Student Engagement and Retained Learning in Higher Education – Faculty Focus

After 13 years of testing higher-order active learning modalities in the classroom, collecting data, building a database, and analyzing student learning results in bi-annual principles of marketing classes, my colleague and I saw two important results emerge. First, all students, regardless of their SES (socio-economic status) standing, showed significantly higher levels of retained learning than the control group which used the time-honored lectures, basic active learning techniques, and case approaches. Secondly, of greater importance, lower-level SES students showed significantly higher retained learning than the general student population. In surveying the literature, significant results have been reported for all students, but not significantly higher than the norm for lower-level SES students.

In the beginning, we decided to build a database measuring what might work better than the currently accepted flipped classroom concept. This step was taken especially since it became more difficult for me over the years in the classroom to keep today’s device-driven students focused beyond 10 seconds or less on an assigned major concept for each of my fifty-minute classes. Retained learning was there but not at the expected levels. In the last 15-20 years as a college professor, especially in marketing and management courses, I have struggled to keep students’ attention, keep them engaged, and keep them on the desired path to improved retained learning of the important course concepts. Fortunately, the classic lecture format, “we speak-you listen”, was replaced years ago by variations of the flipped classroom—students read and studied course content on their own and came to class ready to discuss the major components and issues of what was being studied. However, in this environment, professors must also demonstrate how to apply major concepts to actual problems.

What are the Necessary Steps to Specifically Help SES Students?

We started with guidance from Jean Piaget’s Constructivist Learning Theory, “a process of constructing knowledge based on experience” which embraced active learning modalities as viable examples of the theory. In doing so, we were able to proceed to acceptable testing for TBL/PjBL (Task-Based Learning/Project-Based Learning), simulation, and TBL/PjBL aspects of active learning. With the promise of significantly improving student retained learning experiences, we proceeded to apply the processes to the major classroom concepts under study, connecting them to relevant, firsthand, rigorously applied applications. The best results came from the highest order TBL/PjBL combined modalities where teams chose their own community service projects,

How do you start this process? Some form of testing should be considered if such modalities have not been tried in the past. While our data collection was for entry level classes to a major, it is not recommended that you do that. For that class level, I recommend the simulation modality as offered by some of the major publishers. In addition, we found that problem-based learning, using specific real -world problems, did not work well in my classes as every semester had a different local problem supplied. I also realized that any single problem assignment for the semester in the PjBL format could not be stretched to cover all the semester’s major concepts in the discipline.

Here is a basic framework for using simulation or the TBL/PjBL combination in certain marketing, management, and other related business classes as examples:

1. Choose a Familiar Visualization

To start with, for any principles or like-named introductory classes in the major, regardless of how you introduce the higher order modalities, it is important that you choose a visualization for students to use in their application exercises. In marketing, I use a bookbag (backpack) because of the obvious student familiarity. In management, it might be a company. In sociology, it might be a defined demographic group. In picking a visualization, make sure students have experience with it as a necessary knowledge base on which to build their applications for greater retained learning. This step is essential in all forms of active learning, especially in the higher order modalities. Even though current generational students resist group work, I encouraged them to team up with a study partner (for experience) where they can help each other with content, application, and reporting (even though all submissions must be individual and not just copied from their partner).

If you do not have experience with simulation or TBL/PjBL, it is strongly recommended to test the various forms in a segment of the course, such as in the last four weeks. The easiest way to start this segment is to use a graded, flipped simulation where students apply the application outside of class in that timeframe, with you testing the applied learning results using one concept per week. The way I did this was to have each student submit a written weekly ¾ page minimum response showing how they applied that week’s concept. This will require extra work for you, but the results should be enlightening to your teaching success along with problem areas that should be addressed.

If you choose just simulation, McGraw Hill, for example, uses 8-10 application-based activities (called ABAs) for critical inclusion at set times throughout the semester. Because of the lack of student experience in a general business operation, I allowed three tries at each activity before posting a student’s grade. Two weeks before the end of the semester, I offered students extra credit if they ran the whole marketing process to sell backpacks. Anecdotally, adding TBL/PjBL modalities were too much for them to manage and there was too much group conflict. Thus, a month of individual response classes could be considered a capstone for the class. Now, with the introduction to generative AI, you will now need to review how to let the students use it as a source, just like Google or any other such media; however, be sure to have them quote their source along with properly noting with exact quotes for any AI content used as presented.

For your classes at upper levels such as marketing communications and consumer behavior, use the combined higher order active learning TBL/PjBL model. On the first day of class, define the visualization requirement; however, this will be different for each group as they choose their project. Next, in that same class period, note that they will be working in groups for their TBL/PjBL project with each formed group confirmed by you two weeks in. Define the group size as no more than four students; no less than two students if necessary (class size, resistance etc. may impact choices). Even if you have used or are familiar with higher order modalities in the classroom, you should review the most current literature on how and why components of cooperation must be included (which includes collaboration) and what constitutes an effective TBL/PjBL group. My colleague and I have found that TBL/PjBL projects worked better if developed, designed, and implemented by each student team and with our professorial guidance.

I suggest that you review the details on why the community service project is best, starting with Kuh, Kinzie, Schuh, and Whitt (2005) who first mentioned this approach and recognized its benefits, and Kuh (2008) who expanded on them. See also von Freymann and Cuffe (2020) for six reasons to choose community service projects. Our university has been quite pleased with the outcomes and publicity—along with a sizable donation to one of our schools as a thank you.

2. Form Intentional Teams and Track Achievement Data

Be sure to devise measurement methods for data collection from the start of class to record and compare lower-level SES student achievement versus non-lower-level SES student achievements. Allowing groups to form on their own may not allow for using all the aspects of higher-order cooperation. As an aside: Group composition is often the most difficult step in upper-level classes as many may have their friends in class and who they feel more comfortable working with. A class vote has not worked in the past, so you need to consider setting up the teams yourself guided by your experience with these students and their posted GPAs. You will get some pushbacks, but you should explain in class at the beginning of the semester why this will work better for everyone in the end.

3. Syllabus Inclusions

Syllabus inclusions—promise and provide proper guidance throughout the semester with individual teams outside of class. Also review situations and/or results in class that apply to all the groups’ efforts. Once decided, be sure to update your syllabus with the revised details of what’s expected, how to do it, and what graded parts will be needed.

From the beginning, it’s important to clearly explain in your syllabus how the process will be conducted and assessed, including the required written submissions and final presentation for all higher-order modality applications. For TBL/PjBL, part of the process should require a group response of what was found in the literature review. This will help support the plan recommendation in the final written document and presentation. (Students in the simulation classes should be assigned some individual concept research as well to help them get used to using other sources.) Due to the added student workload, the team research should be submitted by mid-semester for acceptance and separate grading by the professor. Each team member must submit three to five supporting articles with an assessment of how it will help the planning process.

Using the tenants of Constructivist Theory for support, explain to students how the theory should work for them and remind them throughout the course to focus on what they might know, can vicariously relate to, and what they have adapted to, as their base experience in learning the course core concepts as they are revealed by you throughout the semester.

Dr. von Freymann spent thirty years in advertising and marketing as a practitioner working with many companies from high tech to consumer products, national to local. Moving on, Jeff earned an MBA and DBA, each with a marketing emphasis and spent the later part of his career teaching, settling at Wingate University as an associate professor. Using his business experience, he was able to show students how the business process works and why it’s essential for them to be able to apply that learning after graduation.

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May 30, 2025
Conversation and Coursework: Strategies to Engage Undergraduate Students with Course Content – Faculty Focus
Peer-to-peer conversations can help students to make connections with each other and course content. In a course that requires out-of-class reading, that conversation is highly reliant on students doing their part and completing the assigned reading. However, in recent semesters, students engaging in focused reading in which they annotate text is dwindling. There has been a noticeable decline in students’ engagement with course materials, evidenced by reduced annotations and superficial reading habits (Deale & Hyun, 2021; Mizrachi & Salaz, 2022). It seems as if a quick scan of one of the assigned pages is the best effort. Without adequate reading, students will be reluctant, or unable, to participate in class discussions and conversations for lack of understanding and fear of not ‘measuring-up’ to peers (Severe, E., Stalnaker, J., Hubbard, A., Hafen, C. H., & Bailey, E. G., 2024). Subsequently, instructor facilitated classroom conversations intended to enhance understanding of course content may stagnate and falter. Naturally, to maximize student engagement with course content, students must have a degree of self-awareness and desire to do the work assigned to meet the goal of understanding, and mastering, course content.

The frame of content engagement can look different based on course, instructor and level of student. In this case, engagement is on the ‘micro’ level as it offers ideas on what can happen before, during, and after class (Handelsman, M. M., Briggs, W. L., Sullivan, N., & Towler, 2005). While engagement can be observed in many forms, this piece focuses on transactional engagement in which students interact with each other and with the instructor (Zepke & Leach, 2010). As instructors, we can provide the tools to help students engage with content and meaningfully participate in conversation to develop a more robust understanding of coursework.

Course Context

I teach a required, writing-intensive course for students in their junior year of their undergraduate studies. In addition to a heavy writing component, there is also a heavy reading component. While students enjoy the subject matter, they are not terribly thrilled by the amount of reading they are expected to complete. Generally, students start the semester strong, by keeping up with reading, sharing key points and participating in organic content-based conversations. By week four, they hit a wall. There is a notable drop off in the reading and conversations. In an ongoing effort to support students, I altered the clarified expectations and gave more support to students, both in class and beyond.

Choices

Students are busy with layers of competing obligations including classes, work, university organizations and athletics that may impact their time to prepare for class. They may also feel underprepared for reading focused non-fiction, informational texts that require more attention than a casual fiction novel. Being mindful of some students’ sensitivity to what they perceive as inadequate skills, the strategies listed below were shared in class. The whole group presentation and in-class practice encouraged students to try all the tools and self-determine which was most helpful for the time and abilities.

Before Class

Students were introduced to three strategies to use to capture the key points of the assigned reading. The activities provide multiple entry points that allow students to utilize the tool right away.
1. SQ3R. Although this is not a new strategy, I was surprised at how few students had experience working with it. Preparing to join classroom discussions requires intentional preparation. Some students may feel underprepared and think a simple scan of the assigned readings will enable them to join a conversation. While that may work on a superficial level, robust, and meaningful conversation requires proactive work. For students unsure of how to read for maximum understanding a review of the SQ3R strategy is helpful. After a class introduction to the strategy, all students are given a handout with the students so they can utilize as they see fit.
SQ3R
1. Launchpad. For some students, the formulaic nature of a guided strategy, like the SQ3R, may be too limiting. For the student that wants to prepare for in-class conversation, preparing and providing a few open-ended reading reflection questions (launchpad ideas) may be useful. The questions are intentional few in nature so students can focus on responding to one or two and reading to find solid text evidence to support responses.
Launchpad Example 1

Launchpad Example 2
1. Sketchnoting. For students that best express their understanding visually, sketchnoting is a wonderful tool (Parks, 2022). Sketchnoting involves using images, words, and colors to make thinking visible on paper (or a device). When introduced in class as a worthwhile way to capture understating, students may be more likely to try the method rather than just trying to remember what they read.
Sketchnote

The First 10 Minutes of Class

Even with adequate preparation and readings, students get busy with other obligations between assigned coursework and class time. To help students feel prepared, the first 10 minutes (no more) of class is provided as a focused reflection.
1. Focused Reflection. During the 10-minute reflection, students may review their SQ3R notes, add to their reading reflections or sketchnotes. Students may also reread (or, for some, read) the assigned pages. For students that have done it all, they are tasked with doodle time. It’s not as unstructured as it seems.
1. Doodle Time. If a student is reluctant to participate in any of the focused reflections, they are given a doodle paper and three colored pencils. The expectation is to keep the pencils moving during the entire 10-minute period. Some students may start by drawing squiggles or lines, but they do transition into drawing sketches of things they’ve read. Once students believe there is no judgment on their artistic ability and that the focus time is really intended to help them think about the course content, they respond positively to the task. Some have even transitioned from doodling to sketchnoting because, as shared by one student, “it matches the way I think.”
By allowing this 10-minute period to capture thoughts, students have to ability to organize their thoughts and feel confident in their classroom conversation contributions. While it doesn’t level the field completely, and there are still students that do not read, those that do have shared they appreciate the time to refocus their attention on the coursework.

There are only two rules for focused reflection time. The rules are aimed at keeping the classroom distraction free.
1. No talking (to me or each other). The quiet period allows think-time for all students.
1. No devices. The device free period encourages students to independently think about the assigned readings without supporting- or competing- interests. Students are told candidly, I care about their input, not a generative AI summary of the reading.
Building a Safe Space for Conversations

Even with adequate preparation, sharing connections to coursework can be daunting. To support students in the process, a small to big approach is beneficial. First, students are assigned, by the instructor, into conversation corner groups. These groups are intentionally small to promote equitable opportunities to speak. With small classes, I create the groups and intentionally separate any groups of friends to provide opportunities for students to interact with others that may bring new perspectives to the conversation. For larger groups, a random group generator such as https://www.randomlists.com/team-generator can be used. After allowing small group conversation, the students are transitioned into whole group discussions in which they share understanding or respond to instructor posed questions as a group.

Getting the Conversation Going

Students may need help starting content-based conversations. Using a ‘fun’ tool engages students and allows all to have a chance to contribute.
1. Spinning Questions. Pickerwheel (https://pickerwheel.com) or Spin the Wheel (https://spinthewheel.io/#google_vignette) are sites that allow the instructor to enter questions into a spinning wheel. Students spin the wheel and respond to questions in small groups. Students can also create a discussion wheel in their instructor assigned small groups using the questions they created for their SQ3R notes.
1. Conversation Cards. The use of Conversation Cards can help students engage in focused conversation about course content. Students can either draw random card or review all cards and choose the one that is most appealing to them.
Conversation Card Example 1

Conversation Card Example 2

Conversation Card Example 3

Conversation Card Example 4

Conversation Card Example 5

Ending Class

Ending class with an individual account of progress toward mastering course content helps students self-determine their next steps.
1. Accountability Audits. As a ticket out the door, students can complete an exit slip capturing their efforts for the day. Instructors may use the exit tickets as participation grades or as formative assessments to determine how students are understanding content. With larger courses, the instructor may elect to review a random sample of the work, telling students the work is about personal reflection and accountability. Exit slips may be handwritten or they may be done electronically.
Accountability Audit
1. One Last Word. Sometimes extra paper and written work just won’t work. An alternative is to ask individuals, or small groups of students, to suggest one word that sums up the main points of class. The main caveat is no word may be repeated. This one is fun, quick and pushes students to categorize their learning. Please consider a note of caution—this one can quickly turn into a small competition with individuals or groups trying to go first so “their” word is not shared by another group. Don’t worry—it’s all in good fun.
While engaging with content cannot be forced, finding tools that are interesting for students, focused enough for instructors to feel confident valuable class time is used productively and reinforces key points can make learning accessible to all.

Melissa Parks, PhD, is an associate professor of education at Stetson University in Deland, FL. Dr. Parks is an active member of the National Science Teaching Association (NSTA) and is currently a member of the NSTA Early Childhood- Elementary Science Teaching Committee. Her research interests include elementary pedagogies and environmental stewardship.

References

Deale, C. S., & Lee, S. H. (2021). To read or not to read? Exploring the reading habits of hospitality management students. Journal of Hospitality & Tourism Education, 34(1), 45–56. https://doi.org/10.1080/10963758.2020.1868317

Handelsman, M. M., Briggs, W. L., Sullivan, N., & Towler, A. (2005). A measure of college student course engagement. The Journal of Educational Research, 98(3), 184–192. https://doi.org/10.3200/JOER.98.3.184-192

Mizrachi, D., & Salaz, A. M. (2022). Reading format attitudes in the time of COVID. Journal of Academic Librarianship, 48(4), 102552. https://doi.org/10.1016/j.acalib.2022.102552

Parks, M. (2022). Drawing in college: Using sketchnoting to support student engagement. Faculty Focus. Magna Publications.

Severe, E., Stalnaker, J., Hubbard, A., Hafen, C. H., & Bailey, E. G. (2024). To participate or not to participate? A qualitative investigation of students’ complex motivations for verbal classroom participation. PloS one, 19(2), e0297771. https://doi.org/10.1371/journal.pone.0297771

Zepke, N., & Leach, L. (2010). Improving student engagement: Ten proposals for action. Active Learning in Higher Education, 11, 167-177. doi:10.1177/1469787410379680
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May 21, 2025
Bridging the Gap: Active Learning Strategies for Traditional and Online Classrooms – Faculty Focus

There I was, standing in front of my face-to-face Introduction to Psychology class on a sunny Monday afternoon in spring 2024. I was watching as a few of my students took notes while the remaining students sat passively, perhaps hoping my animated gestures and pacing would somehow osmotically transfer my explanation of classical conditioning into their brains. Meanwhile, in my online section of the same course, students worked through carefully crafted modules at their own pace, their participation in discussion boards ebbing and flowing unpredictably. That’s when it hit me—despite years of teaching experience, I’m still searching for ways to spark the same level of engagement whether I’m interacting with students face-to-face or through carefully designed asynchronous activities. These parallel yet distinct teaching environments demand intentional strategies that can adapt while maintaining their power to actively engage students in the learning process.

Rethinking Active Learning for Asynchronous Spaces

Traditional active learning techniques that work beautifully in my face-to-face classes often fall flat in the asynchronous online environment. Take the classic “turn to your neighbor and discuss” prompt that energizes my traditional classes—there’s no direct equivalent when students are logging in at different times throughout the week. Yet simply abandoning these proven engagement strategies isn’t the answer. Instead, we need to reimagine active learning for both spaces, maintaining the core principles while adapting the execution. The key is finding techniques that preserve what makes active learning so powerful—student engagement, peer interaction, and immediate application of concepts—while acknowledging the unique constraints and opportunities of each learning environment.

One of the most powerful techniques in my traditional classroom is the think-pair-share discussion format. Students have those precious moments to gather their thoughts before turning to a classmate to discuss concepts like confirmation bias or the impact of classical conditioning on everyday behavior. The students are energized as pairs merge into small groups, building on each other’s insights before sharing with the whole class. But how do we capture that same collaborative energy in an asynchronous environment?

I’ve found success by structuring online discussions in three distinct phases that mirror the in-person experience. First, students post their initial response to a thought-provoking prompt (the “think” phase). Then, they must meaningfully respond to two classmates’ posts, building on their ideas rather than simply agreeing (the “pair” phase). Finally, students return to their original post and reflect on how their understanding has evolved after engaging with their peers’ perspectives (the “share” phase). The key is crafting prompts that demand critical thinking—instead of asking “What is classical conditioning?” I might ask “How would you use classical conditioning principles to help someone overcome their fear of public speaking?”

Timing matters, too. In my face-to-face class, I allow two to three minutes for individual reflection, five minutes for pair discussions, and 10 minutes for group sharing. Online, I’ve found success with a similar proportional structure: two days for initial posts, three days for peer responses, and two days for final reflections. This creates a rhythm that keeps the discussion moving while accommodating varied schedules.

Designing Collaborative Learning Experiences

Interactive content creation transforms how students engage with course material in both environments. In my traditional classes, I divide students into small groups to create concept maps exploring the relationships between different psychological disorders. Armed with markers and paper, they work collaboratively to connect concepts like anxiety, depression, and trauma responses, discovering links they might have missed studying alone. The energy is palpable as groups share their maps, debating connections and building on each other’s insights.

For my online students, I’ve adapted this activity using collaborative digital tools. Students work in small groups throughout the week to build their concept maps using shared online workspaces. Each student must add at least three concepts and make meaningful connections to their groupmates’ contributions. The asynchronous format actually offers an advantage here – students have time to think deeply about their contributions and can watch their group’s map evolve over several days. I provide specific deadlines for initial contributions and peer feedback to maintain momentum.

I have found that the key to success in either environment lies in providing clear examples of strong concept maps at the outset, establishing specific criteria for meaningful connections, and requiring students to explain their reasoning for each link they create. Deadlines for each phase keep the momentum going, while incorporating peer evaluation into the final grade ensures consistent participation. The resulting maps often reveal insights that surprise both me and my students, demonstrating how collaborative learning can deepen understanding regardless of the teaching modality.

Measuring Engagement and Refining Strategy

One-minute papers serve as powerful self-assessment tools in my traditional classes. For example, at the end of our discussion on memory formation, I ask students to quickly write down the most important concept they learned and one question they still have. This simple exercise reveals gaps in understanding and helps students consolidate their learning. In our next class, I address the most common questions, creating a feedback loop that keeps everyone engaged.

For my online students, I’ve transformed this into structured reflection journals. After completing each module, students must identify their key takeaway and pose one substantive question about the material. The asynchronous format allows for deeper reflection, and I’ve noticed online students often make fascinating connections to their personal experiences. Each week, I compile the most thought-provoking questions into a FAQ document, creating a collaborative resource that benefits the entire class.

Success in both formats require clear guidelines about what constitutes a meaningful reflection versus surface-level observations. Students stating that “the limbic system processes emotions” won’t suffice—they need to explain how this knowledge changes their understanding of human behavior. This approach not only reinforces learning but also develops critical thinking skills that serve students well beyond our course.

Measuring engagement across different learning environments requires that I use a systematic approach. In my traditional classroom, I track participation through observation and collect quick feedback via index cards. For my online students, I monitor not just the frequency but the quality of their discussion posts and collaborative work. The key metrics that I use remain consistent across both spaces: depth of analysis, peer interaction quality, and concept application.

I use a simple framework that examines three elements: initial engagement (participation in discussions or activities), sustained interaction (meaningful responses to peers), and learning application (connecting concepts to real-world scenarios). For each component, I look for evidence of critical thinking rather than mere completion. This approach helps identify which strategies are working and which need adjustment.

Beyond quantitative measures, I pay attention to qualitative indicators like the sophistication of student questions and the complexity of peer-to-peer discussions. These insights guide my refinement of teaching strategies in both environments, ensuring that active learning remains effective regardless of delivery method.

As the spring semester progressed, I saw the impact of the adapted strategies in both my traditional and online psychology classes. Those initially passive students in my traditional class began to engage in our active learning exercises, while my online students built an active learning community through their thoughtful asynchronous interactions. In my experience, the key isn’t choosing between traditional and online teaching methods—it’s understanding how to preserve active learning while adapting strategies to fit each unique environment. By reimagining rather than simply transplanting these strategies, we can create engaging learning experiences that work effectively across both spaces.

Dr. Dunja “Dee” Trunk, a professor of psychology at Bloomfield College of Montclair State University, has a passion for teaching and a genuine belief in the transformative power of education.

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May 5, 2025
Bridging the Gap: Active Learning Strategies for Traditional and Online Classrooms – Faculty Focus

There I was, standing in front of my face-to-face Introduction to Psychology class on a sunny Monday afternoon in spring 2024. I was watching as a few of my students took notes while the remaining students sat passively, perhaps hoping my animated gestures and pacing would somehow osmotically transfer my explanation of classical conditioning into their brains. Meanwhile, in my online section of the same course, students worked through carefully crafted modules at their own pace, their participation in discussion boards ebbing and flowing unpredictably. That’s when it hit me—despite years of teaching experience, I’m still searching for ways to spark the same level of engagement whether I’m interacting with students face-to-face or through carefully designed asynchronous activities. These parallel yet distinct teaching environments demand intentional strategies that can adapt while maintaining their power to actively engage students in the learning process.

Rethinking Active Learning for Asynchronous Spaces

Traditional active learning techniques that work beautifully in my face-to-face classes often fall flat in the asynchronous online environment. Take the classic “turn to your neighbor and discuss” prompt that energizes my traditional classes—there’s no direct equivalent when students are logging in at different times throughout the week. Yet simply abandoning these proven engagement strategies isn’t the answer. Instead, we need to reimagine active learning for both spaces, maintaining the core principles while adapting the execution. The key is finding techniques that preserve what makes active learning so powerful—student engagement, peer interaction, and immediate application of concepts—while acknowledging the unique constraints and opportunities of each learning environment.

One of the most powerful techniques in my traditional classroom is the think-pair-share discussion format. Students have those precious moments to gather their thoughts before turning to a classmate to discuss concepts like confirmation bias or the impact of classical conditioning on everyday behavior. The students are energized as pairs merge into small groups, building on each other’s insights before sharing with the whole class. But how do we capture that same collaborative energy in an asynchronous environment?

I’ve found success by structuring online discussions in three distinct phases that mirror the in-person experience. First, students post their initial response to a thought-provoking prompt (the “think” phase). Then, they must meaningfully respond to two classmates’ posts, building on their ideas rather than simply agreeing (the “pair” phase). Finally, students return to their original post and reflect on how their understanding has evolved after engaging with their peers’ perspectives (the “share” phase). The key is crafting prompts that demand critical thinking—instead of asking “What is classical conditioning?” I might ask “How would you use classical conditioning principles to help someone overcome their fear of public speaking?”

Timing matters, too. In my face-to-face class, I allow two to three minutes for individual reflection, five minutes for pair discussions, and 10 minutes for group sharing. Online, I’ve found success with a similar proportional structure: two days for initial posts, three days for peer responses, and two days for final reflections. This creates a rhythm that keeps the discussion moving while accommodating varied schedules.

Designing Collaborative Learning Experiences

Interactive content creation transforms how students engage with course material in both environments. In my traditional classes, I divide students into small groups to create concept maps exploring the relationships between different psychological disorders. Armed with markers and paper, they work collaboratively to connect concepts like anxiety, depression, and trauma responses, discovering links they might have missed studying alone. The energy is palpable as groups share their maps, debating connections and building on each other’s insights.

For my online students, I’ve adapted this activity using collaborative digital tools. Students work in small groups throughout the week to build their concept maps using shared online workspaces. Each student must add at least three concepts and make meaningful connections to their groupmates’ contributions. The asynchronous format actually offers an advantage here – students have time to think deeply about their contributions and can watch their group’s map evolve over several days. I provide specific deadlines for initial contributions and peer feedback to maintain momentum.

I have found that the key to success in either environment lies in providing clear examples of strong concept maps at the outset, establishing specific criteria for meaningful connections, and requiring students to explain their reasoning for each link they create. Deadlines for each phase keep the momentum going, while incorporating peer evaluation into the final grade ensures consistent participation. The resulting maps often reveal insights that surprise both me and my students, demonstrating how collaborative learning can deepen understanding regardless of the teaching modality.

Measuring Engagement and Refining Strategy

One-minute papers serve as powerful self-assessment tools in my traditional classes. For example, at the end of our discussion on memory formation, I ask students to quickly write down the most important concept they learned and one question they still have. This simple exercise reveals gaps in understanding and helps students consolidate their learning. In our next class, I address the most common questions, creating a feedback loop that keeps everyone engaged.

For my online students, I’ve transformed this into structured reflection journals. After completing each module, students must identify their key takeaway and pose one substantive question about the material. The asynchronous format allows for deeper reflection, and I’ve noticed online students often make fascinating connections to their personal experiences. Each week, I compile the most thought-provoking questions into a FAQ document, creating a collaborative resource that benefits the entire class.

Success in both formats require clear guidelines about what constitutes a meaningful reflection versus surface-level observations. Students stating that “the limbic system processes emotions” won’t suffice—they need to explain how this knowledge changes their understanding of human behavior. This approach not only reinforces learning but also develops critical thinking skills that serve students well beyond our course.

Measuring engagement across different learning environments requires that I use a systematic approach. In my traditional classroom, I track participation through observation and collect quick feedback via index cards. For my online students, I monitor not just the frequency but the quality of their discussion posts and collaborative work. The key metrics that I use remain consistent across both spaces: depth of analysis, peer interaction quality, and concept application.

I use a simple framework that examines three elements: initial engagement (participation in discussions or activities), sustained interaction (meaningful responses to peers), and learning application (connecting concepts to real-world scenarios). For each component, I look for evidence of critical thinking rather than mere completion. This approach helps identify which strategies are working and which need adjustment.

Beyond quantitative measures, I pay attention to qualitative indicators like the sophistication of student questions and the complexity of peer-to-peer discussions. These insights guide my refinement of teaching strategies in both environments, ensuring that active learning remains effective regardless of delivery method.

As the spring semester progressed, I saw the impact of the adapted strategies in both my traditional and online psychology classes. Those initially passive students in my traditional class began to engage in our active learning exercises, while my online students built an active learning community through their thoughtful asynchronous interactions. In my experience, the key isn’t choosing between traditional and online teaching methods—it’s understanding how to preserve active learning while adapting strategies to fit each unique environment. By reimagining rather than simply transplanting these strategies, we can create engaging learning experiences that work effectively across both spaces.

Dr. Dunja “Dee” Trunk, a professor of psychology at Bloomfield College of Montclair State University, has a passion for teaching and a genuine belief in the transformative power of education.

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May 5, 2025
The Role of Microlearning and Andragogy in Enhancing Online Student Engagement – Faculty Focus
The shift to virtual learning, accelerated by the global pandemic, has fundamentally transformed the way education is delivered. As a result, the lasting impact of this transition, along with the necessary adaptations, continues to reshape how education is offered to its target market. With the growing demand for contemporary online education, it is crucial to analyze instructional design – a systematic process that applies psychological principles of human learning to develop effective instructional solutions. This involves evaluating the most effective methods and learning environments to engage students and enhance their achievement of desired learning outcomes.

In this light, designing and facilitating online discussions to increase student engagement, peer connection, and idea exchange, as well as creating a cohesive online learning community for knowledge creation, is far more important for the success of online education. However, delivering online education is crucial in this circumstance as it is really challenging to effectively engage students in the learning process. As students and educators adapt to digital platforms, student engagement has emerged as a key concern in online learning. Traditional classroom techniques often fall short in this new landscape, requiring educators to explore innovative instructional strategies. Two such approaches—microlearning and andragogy—offer promising solutions to enhance student engagement and knowledge retention.

In the traditional learning environment, student engagement levels varied, and educators could easily gauge participation where, in online platforms, the lack of physical presence and direct interaction makes it difficult to assess student engagement. The challenge is further compounded by students balancing their studies with household chores which can lead to decreased motivation if adequate support is not provided by the instructor. Therefore, it is evident that online learning presents a unique set of challenges. Thus, the students who study online must be self-motivated and disciplined enough to work alone as the lack of face-to-face accountability makes it easier for them to drop out without being noticed.

As a result, in today’s competitive lifelong learning landscape, an online course that simply gives information is no better than any other. Hence, instructors should differentiate themselves by offering innovative and interactive online courses that engage, connect, and transform students, enabling them to learn efficiently and effectively from anywhere, converting learning into an enjoyable experience. Consequently, the role of instructors becomes more crucial in designing effective online learning experiences that foster active student participation. Thus, in this turbulent environment, the social, economic, and technical developments elicit new learning concepts and practices, leading to transformations in the way education is delivered to meet the high expectations of the corporate sector. Therefore, the role played by microlearning and andragogy in enhancing online student engagement is crucial in the current context.

Microlearning: Small Bites, Big Impact

Microlearning delivers content in small, focused segments, allowing learners to absorb and retain information more effectively. Research has shown that this approach enables students to process and recall course materials in a more manageable way. Giurgiu (2017) describes that microlearning consists of “bite-sized” educational chunks, usually lasting only a few minutes and designed to cater to the learner’s individual needs. Moreover, microlearning’s approach to delivering short, fine-grained, interconnected and loosely coupled learning activities emphasizes individual learning needs (Singh, 2014). Thus, microlearning fits within the constraints of human cognitive capacity by aligning with how working memory functions. Research highlights that short, structured learning modules enhance engagement and reduce cognitive overload. Hence, in contrast to traditional lengthy lectures, microlearning helps move information from working memory to long-term memory more effectively. In this way, microlearning has become so demanding in current online education.

Another advantage of microlearning is that it addresses the forgetting curve, which predicts that memory retention will deteriorate over time if not reinforced. The forgetting curve illustrates how humans have a tendency to lose nearly half of newly learned information within days or weeks unless they consciously review the learned information. Further, the forgetting curve supports one of the seven types of memory failures: transience which refers to the natural process of forgetting as time passes.

Spaced repetition techniques used in microlearning counteract this decline by reinforcing knowledge retention at periodic intervals. This method involves recalling the same material multiple times over a period, helping to solidify the information in long-term memory with each recall.

Another point where microlearning enhances online student engagement is that it enables students to be entirely focused on and more engaged in learning by allowing them to complete lessons according to their own schedule rather than on someone else’s schedule. Further, in online education as learning takes place outside of the classroom, it has a greater potential for application than typical classroom learning by integrating learning with experience. In this arena, microlearning allows for location-based learning, which can be quite beneficial in increasing students’ engagement in online learning platforms.

Another method of incorporating microlearning to boost online student engagement is to simply include specific sorts of micro lessons within the course itself which can more-or-less run parallel to the course. Microlearning can be integrated into online education using digital tools like Kahoot, GoSoapBox, and Padlet, as well as interactive quizzes ensuring a more interactive and engaging learning experience. Furthermore, designing course materials in concise, digestible formats allows students to integrate learning into their daily schedules more effectively. In this way, microlearning not only brings education back to its original format in some sense but also enhances online student engagement in numerous ways.

Andragogy: Engaging Adult Learners

As the demographics of online learners shift toward older and more diverse groups, understanding how adults learn is essential. Knowles’ andragogical principles emphasize self-directed learning, real-world application, and experiential learning, which are particularly relevant in virtual educational settings. Unlike younger students, adult learners tend to be goal-oriented, seeking education that directly contributes to their career or personal development. They benefit from problem-centered learning and contextual applications, making real-world examples, discussion forums, and interactive activities essential components of online education. Thus, for effective engagement of adult learners in online education, educators must incorporate techniques such as:
- encouraging self-paced learning with embedded resources for independent study
- facilitating peer discussions and collaborative problem-solving exercises and
- utilizing real-world case studies to link theoretical concepts with practical applications.
In practice, andragogy works best when it is tailored to the characteristics of the learners and the learning circumstance.

Combining Microlearning and Andragogy for Optimal Engagement

Recent changes in the macro environment, in the form of advancements in technology and natural transitions, shifted education from traditional platforms to virtual platforms where student engagement is crucial for the success of the learning process. Thus, blending microlearning with andragogical principles creates an effective learning framework that accommodates both cognitive and experiential learning styles. While microlearning breaks down complex topics into smaller units for better retention, andragogy ensures that these units are meaningful and applicable in real-life scenarios. Thus, educators can implement this blended approach by:
- structuring courses into short, interactive modules aligned with real-world applications
- incorporating multimedia elements, such as videos, infographics, and gamified content to enhance engagement
- encouraging student-led discussions and collaborative learning environments
- providing frequent, low-stakes assessments that reinforce learning without adding stress
Addressing Online Learning Challenges

Despite its advantages, online learning presents distinct challenges. Unlike face-to-face settings where instructors can monitor student behavior and adjust teaching methods accordingly, online educators must rely on different strategies to enhance student engagement. Many students struggle with motivation and time management, often due to external distractions. Additionally, the absence of direct supervision can lead to passive learning behaviors, where students disengage without accountability. Thus, to combat these issues, instructors must take a proactive approach in designing online courses. Therefore, ensuring frequent interaction between educators and students, integrating real-world problem-solving activities, and leveraging technological tools can enhance student engagement. Furthermore, the use of formative assessments and timely feedback creates an environment where students feel supported and encouraged.

Conclusion

As online education continues to evolve, educators must adopt innovative strategies to maintain student engagement and improve learning outcomes with microlearning and andragogy provide a structured approach in achieving this goal. Microlearning, as a natural extension of traditional learning, leverages the potential of mobile technology to deliver short, focused lessons that enhance retention while andragogical principles ensure that learning is relevant and applicable to real-life situations. By integrating both approaches, educators can create a dynamic, engaging, and effective online learning experience for learners of all ages. Moreover, the success of online education hinges on its ability to keep learners motivated and actively engaged. Thus, by utilizing a proper combination of traditional and modern instructional strategies, instructors can foster an environment where students not only absorb knowledge but also apply it effectively in their academic and professional journeys.

N.K.L. Silva

Ms. Nilanthige Kaushalya Lakmali Silva is a Lecturer at the Department of Accountancy, University of Kelaniya. She teaches Management, Economics, Human Resource Management, Marketing Management, Strategic Management and Information Management. Ms. Silva holds a B.B.Mgt. (Special) Degree in Accountancy with a First-Class Honours and an M.Sc. in Management with a Merit. Her research interests include major areas of Accounting and Finance, with a particular focus on Sri Lanka’s Capital Markets, Micro and Macro Economic Environment, Corporate Governance, and Corporate Fraud. She has published several research papers in academic journals and conference proceedings. She is a CMA Sri Lanka Passed Finalist and has completed the Diploma in Banking and Finance at the Institute of Bankers of Sri Lanka.

N. P. K. Ekanayake

Ms. Neranjana Priyangani Kumari Ekanayake, a senior lecturer at the University of Kelaniya, teaches Investment and Portfolio Management, Enterprise Resource Planning, Behavioural Finance, and Advanced Management Accounting. She holds a BBA in Accounting (Special) and an MSc in Management (Specialized in Finance) and is a CIMA passed finalist.

References

Bustillo-booth, M. (2019). Applying Andragogy to Online Course Design to Increase Student Engagement and Success. Magna Publications Inc., 1–26.

Dixson, M. D. (2010). Creating Effective Student Engagement in Online Courses: What Do Students Find Engaging? Journal of Scholarship of Teaching and Learning, 10(2), 1–13.

Giurgiu, L. (2017). Microlearning an Evolving Elearning Trend. Scientific Bulletin, 22(1), 18–23. https://doi.org/10.1515/bsaft-2017-0003

Knowles, M. S., Holton III, E. F. & Swanson, R. A. (1998). The Adult Learner: The Definitive Classic in Adult Education and Human Resource Development.

Orlando, J. (2021). Using Microlearning to Improve Student Understanding of Course Content. Magna Publications Inc., 1–18.

Palis, A. G., & Quiros, P. A. (2014). Adult Learning Principles and Presentation Pearls. Middle East African Journal of Ophthalmology, 21(2), 114–122.

Zheng, M. S. (2016). How to Design and Facilitate Online Discussions that Improve Student Learning and Engagement. Magna Publications Inc., 1–18.
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April 16, 2025
AI-Powered Teaching: Practical Tools for Community College Faculty – Faculty Focus
Artificial intelligence (AI) has transitioned from a speculative concept to a transformative tool in higher education, particularly within community colleges. This article explores AI’s historical evolution, critically assesses its benefits and challenges for teaching, and provides evidence-based strategies for faculty to integrate AI effectively. Drawing on the scholarship of teaching and learning (SoTL), it argues that AI can enhance accessibility and efficiency while preserving the human essence of education. A case study from a California community college illustrates these principles in action. Faculty developers and instructors can use this framework to harness AI’s potential, ensuring it supports rather than supplants their pedagogical roles.

Introduction

In Ayad Akhtar’s 2023 play McNeal, ChatGPT performs alongside Robert Downey Jr., a theatrical milestone reflecting artificial intelligence’s (AI) encroachment into domains once deemed uniquely human (Akhtar, 2023, as cited in “From Čapek to ChatGPT”). This echoes Karel Čapek’s R.U.R. (Rossum’s Universal Robots), where artificial beings, created to serve, ultimately undo their creators (Čapek, 1920/2004). A century later, AI is no longer fiction but a dynamic force in community college education, reshaping how faculty teach, and students learn. For instructors serving diverse, often underserved populations, AI raises critical questions: How has it evolved into a pedagogical tool? What are its merits and limitations? How can it enhance teaching without diminishing faculty agency?

This article addresses these questions through a three-part exploration: tracing AI’s historical development, critically evaluating its role in community college classrooms, and offering practical, evidence-based strategies for faculty. Grounded in the scholarship of teaching and learning (SoTL), it expands on prior work (e.g., Davidson, 2021; Pritts, 2023) to position AI as a partner in faculty development. A case study illustrates these principles in practice, aligning with Faculty Focus’s mission to improve teaching and student success in higher education.

Historical Context: AI’s Evolution and Educational Implications

AI’s journey from myth to modernity informs its current educational applications. Ancient tales of Talos, a bronze automaton guarding Europa, and Pygmalion’s animated sculpture reveal humanity’s early fascination with artificial life (Hamilton, 1942). By the 3rd century BC, Greek inventors like Archytas built the steam-powered Flying Pigeon, an early self-operating device (Berryman, 2003). The 18th-century Jaquet-Droz automata—mechanical dolls mimicking human actions—furthered this vision (Riskin, 2016). Philosophers like Descartes and Leibniz speculated on machine cognition, while 19th-century pioneers Babbage and Lovelace laid computing’s foundations (Copeland, 2012).

The modern AI field emerged in 1950 when Alan Turing posed, “Can machines think?” (Turing, 1950), followed by John McCarthy’s naming of “artificial intelligence” (McCarthy, 2007). Early systems like MYCIN, which diagnosed infections using rule-based logic, showcased potential but lacked adaptability (Buchanan & Shortliffe, 1984). The 1990s introduced machine learning, enabling AI to learn from data—email spam filters improved iteratively, marking a shift from static programming (Mitchell, 1997). The 2010s brought deep learning, with neural networks emulating human cognition; Google’s AlphaGo defeated Go champion Lee Sedol in 2016, devising strategies its creators couldn’t predict (Silver et al., 2016).

Since 2020, generative AI—exemplified by OpenAI’s GPT-3 and ChatGPT—has produced human-like text, transforming fields from healthcare to education (Spitale et al., 2023; Topol, 2019). In classrooms, AI’s trajectory is evident: 1980s grading software evolved into 2020s virtual tutors, shifting from peripheral aids to central tools (Baylor & Ryu, 2003). For community college faculty, this history contextualizes AI’s rapid ascent, highlighting its capacity to augment teaching—yet it also underscores the need for critical oversight, a theme explored next.

AI’s integration into community colleges offers transformative potential but demands scrutiny, particularly given the sector’s diverse student body—often first-generation, working adults, or developmental learners (Cohen et al., 2014).

Benefits: Accessibility and Faculty Efficiency
AI enhances accessibility, a cornerstone of community college missions. Hybrid and HyFlex models, supported by AI-driven platforms like Zoom, allow asynchronous learning, vital for students balancing work and family (Quality Matters, 2022). A 2022 report documented a 220% surge in preference for online/hybrid courses (from 9% in 2020 to 29% in 2022), reflecting AI’s role in broadening access (Kelly, 2022, as cited in Quality Matters, 2022). Faculty benefit too: automated grading reduces routine burdens, while NLP tools like ChatGPT provide instant student support, amplifying instructors’ reach (Barnes & Noble Education, 2022; Topol, 2019).

Challenges: Engagement and Skill Development
However, AI’s efficacy hinges on student self-regulation, a persistent challenge where developmental courses predominate (Zimmerman, 2002). Virtual settings often lack in-person dynamics—non-verbal cues, spontaneous discussions—potentially weakening engagement (Quality Matters, 2022). Faculty unease is notable: 54% prefer fully in-person instruction, citing discomfort with digital mediation (Barnes & Noble Education, 2022). Overreliance on AI risks undermining critical thinking; Pritts (2023) warns that tools generating papers may erode analytical skills, echoing SoTL calls for active learning (Davidson, 2021).

Ethical Considerations: Equity and Agency
Ethical dilemmas loom large. Algorithmic bias in grading systems can exacerbate inequities, disproportionately affecting marginalized learners (O’Neil, 2016). Access disparities—unreliable internet or devices—threaten equitable implementation (Means et al., 2020). Spitale et al. (2023) highlight AI’s dual edge: it clarifies concepts but also crafts compelling disinformation, posing risks to academic integrity. Without oversight, AI could shift from facilitator to arbiter, diminishing faculty agency—a concern rooted in Čapek’s cautionary tale (1920/2004). Faculty development must address these tensions to ensure AI serves educational goals.

AI and Academic Integrity: Redrawing Assessment
The rise of AI tools like ChatGPT has forced educators to rethink traditional assessment methods. As discussed in Inside Higher Ed (2025), the prevalence of AI-generated content has made it increasingly difficult to distinguish between student work and machine-generated output. This has led to a growing concern over academic integrity, particularly in community colleges where students may lack the resources or support to navigate these challenges effectively. However, rather than viewing AI as a threat to academic honesty, educators can use this moment to redesign assessments that emphasize critical thinking, creativity, and problem-solving—skills that AI cannot easily replicate. For example, instead of traditional essays, instructors might assign reflective portfolios, oral presentations, or collaborative projects that require students to engage deeply with the material and demonstrate their understanding in ways that go beyond what AI can produce. Faculty development programs should focus on equipping instructors with the tools and strategies to design assessments that are both AI-resistant and pedagogically sound, ensuring that students are evaluated on their ability to think critically and apply knowledge, rather than their ability to generate text.

Practical Applications: Strategies for Faculty Development

To harness AI’s benefits while mitigating risks, community college faculty can adopt five evidence-based strategies, each enhancing teaching practice and student success:
1. AI as a Virtual Tutor: NLP models like ChatGPT offer 24/7 support. A developmental math instructor might direct students to ask, “Explain quadratic equations,” receiving tailored responses, freeing class time for conceptual exploration (Topol, 2019). Faculty can train on integrating such tools via workshops, enhancing their tutoring capacity.
1. Hybrid Course Design: AI platforms enable asynchronous lectures, reserving in-person sessions for interaction. A sociology instructor could post AI-generated summaries on social inequality, then facilitate debates, accommodating diverse schedules (Quality Matters, 2022). Faculty development can include HyFlex training to optimize this balance.
1. Creative Exploration: Tools like MidJourney foster innovation. An English instructor might have students generate visual interpretations of Their Eyes Were Watching God, sparking discussions on symbolism (Hutchison & Woodward, 2014). Workshops on AI-driven creativity can empower faculty to adapt this approach.
1. Personalized Feedback: AI tools like Grammarly flag errors, allowing instructors to refine feedback with comments like, “Expand this argument,” blending efficiency with mentorship (Pritts, 2023). Faculty can learn to leverage AI feedback systems through peer mentoring.
1. Redesigning Assessments for AI-Resistance: In light of the challenges posed by AI-generated content, faculty can adopt assessment strategies that prioritize critical thinking and creativity. For example, instead of traditional essays, instructors might assign reflective portfolios, oral presentations, or collaborative projects that require students to engage deeply with the material and demonstrate their understanding in ways that go beyond what AI can produce. Faculty development programs should focus on equipping instructors with the tools and strategies to design assessments that are both AI-resistant and pedagogically sound, ensuring that students are evaluated on their ability to think critically and apply knowledge, rather than their ability to generate text.
At Sacramento City College (SCC), a 2023 pilot integrated AI into developmental English courses. Faculty used ChatGPT as a virtual tutor, assigning students to draft essays and refine them with AI feedback before submission. Instructors then provided final critiques, focusing on argumentation over mechanics. A survey of 50 students found 78% felt more confident in writing, while faculty reported a 30% reduction in grading time (SCC Faculty Development Report, 2023, unpublished). However, 20% of students lacked reliable internet, prompting SCC to expand Wi-Fi access. This case underscores AI’s potential to enhance teaching efficiency and student outcomes, while highlighting equity challenges faculty developers must address.

In response to concerns about AI-generated content, SCC also piloted a new assessment model in which students were required to present their essays orally and engage in peer review sessions. This approach not only reduced the risk of AI-generated submissions but also fostered a deeper understanding of the material through active engagement and collaboration. Faculty reported that students were more invested in the learning process and demonstrated higher levels of critical thinking and creativity in their work.

Conclusion

AI’s evolution—from ancient myths to ChatGPT’s theatrical debut—mirrors humanity’s quest to extend its capabilities. In community colleges, it offers accessibility and efficiency, yet risks eroding the relational core of teaching. Faculty development can bridge this gap by equipping instructors with strategies to integrate AI as a tutor, design tool, creative aid, feedback enhancer, and discussion catalyst. The SCC case study illustrates this balance in practice. As Davidson (2021) and Pritts (2023) argue, the goal is to delegate routine tasks to AI while reserving inspiration and mentorship for faculty—a principle rooted in SoTL. By fostering AI literacy and ethical oversight, faculty developers can ensure AI amplifies, rather than supplants, the human element of education.

Moreover, as the Inside Higher Ed article highlights, the rise of AI-generated content presents an opportunity to rethink assessment practices. By designing assessments that emphasize critical thinking, creativity, and collaboration, faculty can ensure that students are evaluated on their ability to engage deeply with the material, rather than their ability to generate text. This shift not only addresses concerns about academic integrity but also prepares students for a future in which AI is an integral part of the workforce.

David E. Balch, PhD, is a professor at Rio Hondo College and has published articles in the areas of ethics, humor, distance education, and AI.

Robert Blanck, MA, has 35 years’ experience in teaching and administration within the fields of education and business. Blanck earned the Lean Six Sigma Black Belt designation for industrial quality control.

Note: this article used collaboration between the human authors and the AI program Grok (xAI).

References

Barnes & Noble Education. (2022, June 28). College 2030™ report: Higher education’s preparation post-pandemic. Business Wire. https://www.businesswire.com/news/home/20220628005207/en/

Baylor, A. L., & Ryu, J. (2003). The API model: Using AI to enhance learning. Educational Technology Research and Development, 51(4), 89–102. https://doi.org/10.1007/BF02504554

Buchanan, B. G., & Shortliffe, E. H. (1984). Rule-based expert systems: The MYCIN experiments of the Stanford Heuristic Programming Project. Addison-Wesley.

Čapek, K. (2004). R.U.R. (Rossum’s Universal Robots) (C. Novack, Trans.). Penguin Classics. (Original work published 1920)

Davidson, C. N. (2021). The new education: How to revolutionize the university to prepare students for a world in flux. Basic Books.

EDUCAUSE. (2023). AI and academic integrity: Rethinking assessment. https://www.educause.edu

Hutchison, A., & Woodward, L. (2014). A planning cycle for integrating digital technology into literacy instruction. The Reading Teacher, 67(6), 455–464. https://doi.org/10.1002/trtr.1225

McCarthy, J. (2007). What is artificial intelligence? Stanford University. http://jmc.stanford.edu/articles/whatisai/whatisai.pdf

Means, B., Bakia, M., & Murphy, R. (2020). Learning online: What research tells us about whether, when and how. Routledge.

Mitchell, T. M. (1997). Machine learning. McGraw-Hill.

O’Neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown. Note: changed the date from 2016 to 2017

Pritts, N. (2023). AI and the future of critical thinking in higher education. Journal of Educational Technology, 40(2), 123–135.

Quality Matters. (2022). 2022 higher education trends report. https://www.qualitymatters.org/research

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489. https://doi.org/10.1038/nature16961

Spitale, G., Biller-Andorno, N., & Germani, F. (2023). AI model GPT-3 (dis)informs us better than humans. Science Advances, 9(5), Article eade7823. https://doi.org/10.1126/sciadv.ade7823

Topol, E. J. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
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March 31, 2025
AI-Powered Teaching: Practical Tools for Community College Faculty – Faculty Focus
Artificial intelligence (AI) has transitioned from a speculative concept to a transformative tool in higher education, particularly within community colleges. This article explores AI’s historical evolution, critically assesses its benefits and challenges for teaching, and provides evidence-based strategies for faculty to integrate AI effectively. Drawing on the scholarship of teaching and learning (SoTL), it argues that AI can enhance accessibility and efficiency while preserving the human essence of education. A case study from a California community college illustrates these principles in action. Faculty developers and instructors can use this framework to harness AI’s potential, ensuring it supports rather than supplants their pedagogical roles.

Introduction

In Ayad Akhtar’s 2023 play McNeal, ChatGPT performs alongside Robert Downey Jr., a theatrical milestone reflecting artificial intelligence’s (AI) encroachment into domains once deemed uniquely human (Akhtar, 2023, as cited in “From Čapek to ChatGPT”). This echoes Karel Čapek’s R.U.R. (Rossum’s Universal Robots), where artificial beings, created to serve, ultimately undo their creators (Čapek, 1920/2004). A century later, AI is no longer fiction but a dynamic force in community college education, reshaping how faculty teach, and students learn. For instructors serving diverse, often underserved populations, AI raises critical questions: How has it evolved into a pedagogical tool? What are its merits and limitations? How can it enhance teaching without diminishing faculty agency?

This article addresses these questions through a three-part exploration: tracing AI’s historical development, critically evaluating its role in community college classrooms, and offering practical, evidence-based strategies for faculty. Grounded in the scholarship of teaching and learning (SoTL), it expands on prior work (e.g., Davidson, 2021; Pritts, 2023) to position AI as a partner in faculty development. A case study illustrates these principles in practice, aligning with Faculty Focus’s mission to improve teaching and student success in higher education.

Historical Context: AI’s Evolution and Educational Implications

AI’s journey from myth to modernity informs its current educational applications. Ancient tales of Talos, a bronze automaton guarding Europa, and Pygmalion’s animated sculpture reveal humanity’s early fascination with artificial life (Hamilton, 1942). By the 3rd century BC, Greek inventors like Archytas built the steam-powered Flying Pigeon, an early self-operating device (Berryman, 2003). The 18th-century Jaquet-Droz automata—mechanical dolls mimicking human actions—furthered this vision (Riskin, 2016). Philosophers like Descartes and Leibniz speculated on machine cognition, while 19th-century pioneers Babbage and Lovelace laid computing’s foundations (Copeland, 2012).

The modern AI field emerged in 1950 when Alan Turing posed, “Can machines think?” (Turing, 1950), followed by John McCarthy’s naming of “artificial intelligence” (McCarthy, 2007). Early systems like MYCIN, which diagnosed infections using rule-based logic, showcased potential but lacked adaptability (Buchanan & Shortliffe, 1984). The 1990s introduced machine learning, enabling AI to learn from data—email spam filters improved iteratively, marking a shift from static programming (Mitchell, 1997). The 2010s brought deep learning, with neural networks emulating human cognition; Google’s AlphaGo defeated Go champion Lee Sedol in 2016, devising strategies its creators couldn’t predict (Silver et al., 2016).

Since 2020, generative AI—exemplified by OpenAI’s GPT-3 and ChatGPT—has produced human-like text, transforming fields from healthcare to education (Spitale et al., 2023; Topol, 2019). In classrooms, AI’s trajectory is evident: 1980s grading software evolved into 2020s virtual tutors, shifting from peripheral aids to central tools (Baylor & Ryu, 2003). For community college faculty, this history contextualizes AI’s rapid ascent, highlighting its capacity to augment teaching—yet it also underscores the need for critical oversight, a theme explored next.

AI’s integration into community colleges offers transformative potential but demands scrutiny, particularly given the sector’s diverse student body—often first-generation, working adults, or developmental learners (Cohen et al., 2014).

Benefits: Accessibility and Faculty Efficiency
AI enhances accessibility, a cornerstone of community college missions. Hybrid and HyFlex models, supported by AI-driven platforms like Zoom, allow asynchronous learning, vital for students balancing work and family (Quality Matters, 2022). A 2022 report documented a 220% surge in preference for online/hybrid courses (from 9% in 2020 to 29% in 2022), reflecting AI’s role in broadening access (Kelly, 2022, as cited in Quality Matters, 2022). Faculty benefit too: automated grading reduces routine burdens, while NLP tools like ChatGPT provide instant student support, amplifying instructors’ reach (Barnes & Noble Education, 2022; Topol, 2019).

Challenges: Engagement and Skill Development
However, AI’s efficacy hinges on student self-regulation, a persistent challenge where developmental courses predominate (Zimmerman, 2002). Virtual settings often lack in-person dynamics—non-verbal cues, spontaneous discussions—potentially weakening engagement (Quality Matters, 2022). Faculty unease is notable: 54% prefer fully in-person instruction, citing discomfort with digital mediation (Barnes & Noble Education, 2022). Overreliance on AI risks undermining critical thinking; Pritts (2023) warns that tools generating papers may erode analytical skills, echoing SoTL calls for active learning (Davidson, 2021).

Ethical Considerations: Equity and Agency
Ethical dilemmas loom large. Algorithmic bias in grading systems can exacerbate inequities, disproportionately affecting marginalized learners (O’Neil, 2016). Access disparities—unreliable internet or devices—threaten equitable implementation (Means et al., 2020). Spitale et al. (2023) highlight AI’s dual edge: it clarifies concepts but also crafts compelling disinformation, posing risks to academic integrity. Without oversight, AI could shift from facilitator to arbiter, diminishing faculty agency—a concern rooted in Čapek’s cautionary tale (1920/2004). Faculty development must address these tensions to ensure AI serves educational goals.

AI and Academic Integrity: Redrawing Assessment
The rise of AI tools like ChatGPT has forced educators to rethink traditional assessment methods. As discussed in Inside Higher Ed (2025), the prevalence of AI-generated content has made it increasingly difficult to distinguish between student work and machine-generated output. This has led to a growing concern over academic integrity, particularly in community colleges where students may lack the resources or support to navigate these challenges effectively. However, rather than viewing AI as a threat to academic honesty, educators can use this moment to redesign assessments that emphasize critical thinking, creativity, and problem-solving—skills that AI cannot easily replicate. For example, instead of traditional essays, instructors might assign reflective portfolios, oral presentations, or collaborative projects that require students to engage deeply with the material and demonstrate their understanding in ways that go beyond what AI can produce. Faculty development programs should focus on equipping instructors with the tools and strategies to design assessments that are both AI-resistant and pedagogically sound, ensuring that students are evaluated on their ability to think critically and apply knowledge, rather than their ability to generate text.

Practical Applications: Strategies for Faculty Development

To harness AI’s benefits while mitigating risks, community college faculty can adopt five evidence-based strategies, each enhancing teaching practice and student success:
1. AI as a Virtual Tutor: NLP models like ChatGPT offer 24/7 support. A developmental math instructor might direct students to ask, “Explain quadratic equations,” receiving tailored responses, freeing class time for conceptual exploration (Topol, 2019). Faculty can train on integrating such tools via workshops, enhancing their tutoring capacity.
1. Hybrid Course Design: AI platforms enable asynchronous lectures, reserving in-person sessions for interaction. A sociology instructor could post AI-generated summaries on social inequality, then facilitate debates, accommodating diverse schedules (Quality Matters, 2022). Faculty development can include HyFlex training to optimize this balance.
1. Creative Exploration: Tools like MidJourney foster innovation. An English instructor might have students generate visual interpretations of Their Eyes Were Watching God, sparking discussions on symbolism (Hutchison & Woodward, 2014). Workshops on AI-driven creativity can empower faculty to adapt this approach.
1. Personalized Feedback: AI tools like Grammarly flag errors, allowing instructors to refine feedback with comments like, “Expand this argument,” blending efficiency with mentorship (Pritts, 2023). Faculty can learn to leverage AI feedback systems through peer mentoring.
1. Redesigning Assessments for AI-Resistance: In light of the challenges posed by AI-generated content, faculty can adopt assessment strategies that prioritize critical thinking and creativity. For example, instead of traditional essays, instructors might assign reflective portfolios, oral presentations, or collaborative projects that require students to engage deeply with the material and demonstrate their understanding in ways that go beyond what AI can produce. Faculty development programs should focus on equipping instructors with the tools and strategies to design assessments that are both AI-resistant and pedagogically sound, ensuring that students are evaluated on their ability to think critically and apply knowledge, rather than their ability to generate text.
At Sacramento City College (SCC), a 2023 pilot integrated AI into developmental English courses. Faculty used ChatGPT as a virtual tutor, assigning students to draft essays and refine them with AI feedback before submission. Instructors then provided final critiques, focusing on argumentation over mechanics. A survey of 50 students found 78% felt more confident in writing, while faculty reported a 30% reduction in grading time (SCC Faculty Development Report, 2023, unpublished). However, 20% of students lacked reliable internet, prompting SCC to expand Wi-Fi access. This case underscores AI’s potential to enhance teaching efficiency and student outcomes, while highlighting equity challenges faculty developers must address.

In response to concerns about AI-generated content, SCC also piloted a new assessment model in which students were required to present their essays orally and engage in peer review sessions. This approach not only reduced the risk of AI-generated submissions but also fostered a deeper understanding of the material through active engagement and collaboration. Faculty reported that students were more invested in the learning process and demonstrated higher levels of critical thinking and creativity in their work.

Conclusion

AI’s evolution—from ancient myths to ChatGPT’s theatrical debut—mirrors humanity’s quest to extend its capabilities. In community colleges, it offers accessibility and efficiency, yet risks eroding the relational core of teaching. Faculty development can bridge this gap by equipping instructors with strategies to integrate AI as a tutor, design tool, creative aid, feedback enhancer, and discussion catalyst. The SCC case study illustrates this balance in practice. As Davidson (2021) and Pritts (2023) argue, the goal is to delegate routine tasks to AI while reserving inspiration and mentorship for faculty—a principle rooted in SoTL. By fostering AI literacy and ethical oversight, faculty developers can ensure AI amplifies, rather than supplants, the human element of education.

Moreover, as the Inside Higher Ed article highlights, the rise of AI-generated content presents an opportunity to rethink assessment practices. By designing assessments that emphasize critical thinking, creativity, and collaboration, faculty can ensure that students are evaluated on their ability to engage deeply with the material, rather than their ability to generate text. This shift not only addresses concerns about academic integrity but also prepares students for a future in which AI is an integral part of the workforce.

David E. Balch, PhD, is a professor at Rio Hondo College and has published articles in the areas of ethics, humor, distance education, and AI.

Robert Blanck, MA, has 35 years’ experience in teaching and administration within the fields of education and business. Blanck earned the Lean Six Sigma Black Belt designation for industrial quality control.

Note: this article used collaboration between the human authors and the AI program Grok (xAI).

References

Barnes & Noble Education. (2022, June 28). College 2030™ report: Higher education’s preparation post-pandemic. Business Wire. https://www.businesswire.com/news/home/20220628005207/en/

Baylor, A. L., & Ryu, J. (2003). The API model: Using AI to enhance learning. Educational Technology Research and Development, 51(4), 89–102. https://doi.org/10.1007/BF02504554

Buchanan, B. G., & Shortliffe, E. H. (1984). Rule-based expert systems: The MYCIN experiments of the Stanford Heuristic Programming Project. Addison-Wesley.

Čapek, K. (2004). R.U.R. (Rossum’s Universal Robots) (C. Novack, Trans.). Penguin Classics. (Original work published 1920)

Davidson, C. N. (2021). The new education: How to revolutionize the university to prepare students for a world in flux. Basic Books.

EDUCAUSE. (2023). AI and academic integrity: Rethinking assessment. https://www.educause.edu

Hutchison, A., & Woodward, L. (2014). A planning cycle for integrating digital technology into literacy instruction. The Reading Teacher, 67(6), 455–464. https://doi.org/10.1002/trtr.1225

McCarthy, J. (2007). What is artificial intelligence? Stanford University. http://jmc.stanford.edu/articles/whatisai/whatisai.pdf

Means, B., Bakia, M., & Murphy, R. (2020). Learning online: What research tells us about whether, when and how. Routledge.

Mitchell, T. M. (1997). Machine learning. McGraw-Hill.

O’Neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown. Note: changed the date from 2016 to 2017

Pritts, N. (2023). AI and the future of critical thinking in higher education. Journal of Educational Technology, 40(2), 123–135.

Quality Matters. (2022). 2022 higher education trends report. https://www.qualitymatters.org/research

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489. https://doi.org/10.1038/nature16961

Spitale, G., Biller-Andorno, N., & Germani, F. (2023). AI model GPT-3 (dis)informs us better than humans. Science Advances, 9(5), Article eade7823. https://doi.org/10.1126/sciadv.ade7823

Topol, E. J. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
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March 31, 2025
Supporting Non-Major Biology Students: Making the Classroom YOUnique – Faculty Focus
Supporting biology courses for non-majors involves addressing the diverse needs of students with little intrinsic motivation to study the subject. Many take these courses as part of general education, not personal interest, creating a challenge in maintaining engagement and fostering deeper understanding (Haak et al. 2011). Non-majors often struggle to connect course content to their lives, making it harder to see the subject’s value (Labrov and Singer, 2010). These students come from varied academic backgrounds, resulting in different levels of preparedness and confidence (Tobias 1990; Gormally and Heil 2022). Some may experience anxiety or feel science is irrelevant to their careers (Rice et al. 2013). 

To address these challenges, educators must adopt approaches that make biology accessible and meaningful. By creating a learning environment tailored to various majors, addressing barriers, and demonstrating real-world applications, instructors can make the classroom “YOUnique.” Making the classroom “YOUnique” tailors the experience to individual needs, fostering belonging and showing biology’s relevance. This article explores how strategies—fostering engagement, connecting biology to real-world contexts, and to students’ majors—can support non-major biology students. These strategies will help students build confidence to apply biological knowledge in their careers. 

1. Fostering Engagement in Non-Major Biology Students through a Studio Environment

Engaging students not inherently drawn to biology requires innovative teaching methods. Active learning strategies, where students actively engage in the learning process, promote participation and retention (Freeman et al. 2014). These include group discussions, hands-on experiments, problem-solving activities, and creative assessments that encourage critical thinking and peer collaboration. 

A case study of engagement is the studio-style introductory biology classroom at Kansas State University (Manhattan, KS, USA). This innovative teaching environment blends engaged lectures with hands-on, collaborative activities, with the instructor as a facilitator. The studio integrates lecture, lab, and group work in a flexible learning environment, allowing students to actively participate in problem-solving, discussions, and projects, and it supports diverse learning activities and styles. This approach fosters an interactive environment which is beneficial for student learning. 

Here are some ways the studio-style classroom helps non-major biology students: 
- Active Learning: The classroom setup encourages active engagement, helping non-majors grasp complex biological concepts through hands-on experiences, computer tutorials, and group activities, rather than passive listening. 
- Collaborative Learning: Students work in small groups, promoting peer interaction and problem-solving, which supports non-majors who may feel less confident in their knowledge. 
- Integrated Learning: Combining lecture and lab time allows students to apply theoretical knowledge to practical experiments, helping solidify learning and making complex concepts more understandable. 
- Personalized Attention: The instructor can circulate among groups, providing targeted support and reassurance, which is especially beneficial for non-majors needing more guidance. 
- Flexible Space: The studio classroom’s flexibility to use various technology allows instructors to adapt teaching styles and activities, ensuring responsiveness to the diverse needs of non-major students.
2. Connecting Biology to Real-World Contexts

For non-majors, understanding how biology applies to everyday life is crucial for maintaining interest and motivation. Without a clear connection to their personal or professional lives, students may view biology as abstract or irrelevant. By demonstrating how biological principles impact their world, educators can foster greater engagement. When students recognize the value of what they are learning—whether through daily applications or career relevance—they are more likely to stay motivated, actively participate, and retain information. 

A highly effective way to establish relevance is through interdisciplinary connections that show how biology extends beyond the classroom (Beers and Jensen 2009; Morales and Boulware 2020). Prior to the first day of class, there is a discussion board asking why biology is important to them, and instructors use the responses as an avenue to connect biology and their lives during a class discussion. For every class period, the instructor connects what they are learning to their lives. For instance, using a computer simulation called “Kansas feeds the world” students can explore how photosynthesis and cellular respiration (cell biology unit), along with energy and nutrient dynamics (ecology unit), interconnect with real world issues. In the genetics unit, students connect molecular genetics and biotechnology using an assignment where the students develop a biotechnology product and generate a print ad or video commercial. The ads are shared during class, showcasing how biological processes can drive innovations like medical devices and biofuels. Each unit follows a pattern of building basic knowledge followed by exploring implications and applications to people’s lives.  

Another effective method is case-base active learning (Chaplin 2009; Dewi and Rahayu 2023). Presenting real-world scenarios, like infectious diseases or genetic testing, makes biology come alive. For example, the instructors use a readily available MMR vaccine autism case study to explore the process of science, and let students come to their own conclusions about vaccine efficacy. 

Inviting guest speakers from diverse fields can help students see biology’s broad applications (D’Andrea and Mertens 2006; Marx et al. 2021). For example, we have graduate students presenting on the benefits of “long-term ecological research stations” (LTERs) such as the Konza Prairie Biological Center (Kansas, USA), or the Scientist Spotlights, which is biography about them and their research showing that biologists are real people. These speakers not only provide real-life examples but also inspire students to think critically about how biological knowledge could inform their own career aspirations. Hearing from professionals at the intersection of biology and their disciplines broadens students’ perspectives, reinforcing biology’s fundamental role in life and work.

3. Demonstrating Relevance to Students’ Majors

For non-majors, a significant barrier to success in biology is the perception that the subject lacks relevance to their lives or careers (Gormally and Hall 2022). Students in fields like business, art, or education may struggle to see how topics like cellular respiration or ecology apply to their goals, which can hinder motivation and engagement. 

To address this challenge, educators must emphasize biology’s connections to different majors and to generate connections that have career relevance. Highlighting interdisciplinary applications, such as the role of biology in public health (communications majors), technology (computer science majors), or environmental policy (pollical science majors), can help students appreciate its broad impact. For example, a lesson on climate change might include discussions on the biological effects of carbon emissions, connecting the topic to social and economic issues like the displacement of communities due to rising sea levels and the economic costs of extreme weather events, such as hurricanes and wildfires. 

Another effective approach is personalized assignments (Driessen et al. 2024). Personalized projects allow students to explore biology in ways that resonate with their own lives. One such assignment is a “Biology Connections” activity, where students reflect on how biological concepts connect to their major and career. For example, a business student might describe how ecological sustainability impacts corporate strategies, while an education major might reflect on the importance of understanding human development in teaching practices. This reflection promotes motivation, showing students that biology has practical applications in their daily lives and careers. These personalized assignments allow instructors to make biology “YOUnique”. 

Conclusion

Supporting non-major biology students requires efforts to foster engagement, connect material to real-world contexts, and demonstrate relevance to different majors and backgrounds. Active learning, real-world connections, and ties to their major create a supportive environment where students thrive. By addressing unique challenges and interests, educators can empower non-majors to appreciate biology’s relevance and develop skills beyond the classroom. When students see content as meaningful and tied to their lives and careers, they are more likely to engage, retain knowledge, and apply it. This approach enhances understanding and equips them with critical thinking and problem-solving skills essential in any profession. By “Making the Classroom YOUnique,” educators can foster an environment where students feel valued, leading to greater motivation, academic achievement, and personal growth. A “YOUnique” approach helps students connect with the subject matter and realize their potential as learners in the biology classroom, regardless of their major. 

Jack Sytsma is a doctoral graduate teaching assistant in the Division of Biology at Kansas State University. While his research is at the forefront of understanding how plants respond to climate, Jack is equally passionate about teaching. He finds great fulfillment in educating and mentoring undergraduate students, particularly in courses that bridge ecological principles with real-world applications. With aspirations of becoming a teaching professor in biology, Jack aims to inspire the next generation of scientists by fostering curiosity and a deeper understanding of the subject.

Robert ‘Robbie’ Bear is a Senior Biology Instructor in the Division of Biology at Kansas State University. Robbie started teaching 33 years ago as an undergraduate and ever since then he has synthesized his experiences on either side of the podium into his pedagogy of an inquiry-based student centered learning. He is currently teaching his dream course and finds happiness and purpose in people discovering the importance of Biology in their life. In the remaining years of his career, Robbie wants future educators (the Jack Sytsma’s) to have the foundational pedagogy that allows them to explore, grow, and succeed as teachers.

Eve S. McCulloch, PhD, is a teaching associate professor in the Division of Biology at Kansas State University. Eve has been teaching biology courses for 12 years, ranging from introductory to upper-level classes. Eve’s research background is in mammalogy, ecology, and conservation genetics. She is in her dream job, working to support undergraduate students both in and outside of the classroom. Eve is passionate about making the biological sciences accessible to students of all backgrounds and fields of interest, and promoting undergraduate student success while at university at beyond.

References  

Armstrong, N., and S. M. Chang. “Teaching Non-Majors Biology: Pedagogical Challenges and Opportunities.” Journal of College Science Teaching (2007).

Beers, S. F., and M. Jensen. “Integrating Science and Literacy: Enhancing Understanding of Science Through Interdisciplinary Instruction.” Science Scope 32, no. 2 (2009): 20-27.

Brewer, C. A., and D. Smith. Vision and Change in Undergraduate Biology Education: A Call to Action (2011).

Chaplin, S. “Assessment of the Impact of Case Studies on Student Learning Gains in an Introductory Biology Course.” CBE—Life Sciences Education 8, no. 3 (2009): 297–306.

Cooper, K. M., et al. “The Impact of Active Learning on Retention of Non-Majors in Biology” (2019).

Cotner, S., S. Thompson, and R. Wright. “Do Biology Majors Really Differ from Non–STEM Majors?” CBE—Life Sciences Education 16, no. 3 (2017): ar48.

D’Andrea, M. L., and D. M. Mertens. “Creating Meaningful Connections: The Role of Guest Speakers in the Classroom.” Journal of College Science Teaching 35, no. 1 (2006): 40–45.

Dewi, C.A., and S. Rahayu. “Implementation of Case-Based Learning in Science Education: A Systematic Review” (2023).

Driessen, E. P., K. E. Walker, T. Hallman, A. Casper, S. L. Eddy, J. R. Schneider, and A. K. Lane. “‘It’s Been a Process’: A Multiple Case Study of Biology Instructor Efforts to Reform Their Sex and Gender Curriculum to be More Inclusive of Students with Queer Genders and Intersex Students.” CBE—Life Sciences Education 23, no. 4 (2024): ar51.

Freeman, S., et al. “Active Learning Increases Student Performance in Science, Engineering, and Mathematics.” Proceedings of the National Academy of Sciences 111, no. 23 (2014): 8410–8415.

Gormally, C., and A. Heil. “A Vision for University Biology Education for Non-Science Majors.” CBE—Life Sciences Education 21, no. 4 (2022): es5.

Haak, D. C., J. HilleRisLambers, E. Pitre, and S. Freeman. “Increased Structure and Active Learning Reduce the Achievement Gap in Introductory Biology.” Science 332, no. 6034 (2011): 1213-1216.

Labov, J. B., and S. R. Singer. “Community Colleges in the Evolving STEM Education Landscape” (2010).

Marx, M. A., R. L. Glaser, C. E. Moran, and K. P. Tucker. “A Creative Model for an Interdisciplinary Approach to Service-Learning.” Integrative and Comparative Biology 61, no. 3 (2021): 1028-1038.

Morales, E., and R. Boulware. “A Cross-Disciplinary Approach to Teaching Biology and Environmental Science: The Intersection of Biology and Policy.” Journal of College Science Teaching 49, no. 6 (2020): 40–47.

Rice, L., J. M. Barth, R. E. Guadagno, G. P. Smith, and D. M. McCallum. “The Role of Social Support in Students’ Perceived Abilities and Attitudes Toward Math and Science.” Journal of Youth and Adolescence 42, no. 7 (2013): 1028–1040.

Tobias, S. They’re Not Dumb, They’re Different: Stalking the Second Tier (1990).
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March 12, 2025
Tired of Awkward Silences? Upgrade your Think-Pair-Share – Faculty Focus

I’ve tried a few different techniques for class discussions over the years, some that I’ve written about previously in Faculty Focus. In recent years, I’ve dialed in a 3-step process for discussion in my face-to-face classes, which has helped me create an engaged classroom climate. It’s Think-Pair-Share (TPS), with some upgrades.

I refined my TPS process after attending Jeremy Rentz’s workshop “Put Your Hammer Down” (detailed in the Best of the 2021 Teaching Professor Conference). His key idea was “The person doing the learning is the person doing the work.” I thought about how to get my students to do more “work” in my large lecture-based classes. Although I knew the active learning benefits of a technique like TPS, I struggled to implement it well. There were often too many awkward silences for TPS to be as engaging as I would have liked. After Jeremy’s workshop, I made some upgrades to TPS that invigorated it for students and for me.

Step #1: Think
Upgrade: Discussion Bubbles

Instead of posing my discussion question after I’ve covered the content on my slide, I now put the question in a Discussion Bubble next to the content. Prior to implementing this, I would review the slide and then pose my question, either a specific one, or something general like “anyone have anything to add?” Then there would be the awkward silence while students think, and I would try to be chill. Having a Discussion Bubble primes students to think and be ready to share (in small or large groups) once I’m done reviewing the slide. It not only decreases the awkwardness (which seems to feed off itself), but it also increases small and large group discussion participation.

Sometimes these discussion bubbles include a polling question that I use to record and display students’ opinions (e.g., Do you prefer a, b, or c?). Students can then see that they are not alone in their views and are more confident to share their perspectives in Steps 2 and 3.

Step #2: Pair
Upgrade: 1-Minute Timer

I give students one minute to talk with classmates about their thoughts on the discussion bubble or their answer to the polling question. On the very first day, I tell students that I will use my watch to set a 1-minute timer and that we’ll have a few of these in every class. I believe that these short, reliable segments help students feel safe and avoid too-lengthy, awkward small-talk. At the end of the one-minute paired discussion, I don’t check to see if they need more time as I have large classes, and the level of discussion is variable. In recent course surveys (classes of 90-175 students), about 70% of students wanted to keep these 1-minute discussions and only 13% wanted to change them (some were undecided). Further, about 44% of students agreed that these 1-minute discussions helped them feel more connected to their classmates (42% had mixed or neutral feelings) and 63% said they were comfortable sharing in class. So, it’s not perfect for everyone, but it’s benefiting many students. Also, I don’t have students pair off every time that I have a discussion bubble—sometimes I skip Step 2 and go straight to Step 3.

After Step 1 and maybe 2, I incentivize large group discussion with an extra credit point for sharing aloud. I’ve written previously about my Fired-Up and Ready to Discuss process, which involves giving students up to 1 point each class (and 3-5 points total for the semester) for sharing aloud. I have undergraduate teaching assistants present and poised to enter the student’s point into the gradebook as soon as they say their name and share (and if they forget, they can find the teaching assistants after class). It’s an immediate reward that encourages sharing in a large class. Only about ½ of students in my large classes share aloud and earn Fired-up Points, but in a class of 100 students, getting 50 to talk aloud is enough to create an engaging classroom climate. In recent surveys, 70% of the students want to keep Fired Up (and only about 10% want to change it).

This upgraded TPS not only helps students “do the work,” but it also makes for a fun and engaging class. It gives my lectures a predicable rhythm that creates psychological safety for student engagement. It also helps me stay confident and relaxed, which isomorphically encourages students to relax and take the risk to share their thoughts.

Dr. Ashley Harvey is a professor in the Department of Human Development and Family Studies at Colorado State University (CSU). Since 2007, she has taught more than 10,000 students across 15 different courses in over 150 undergraduate and graduate sections. Dr. Harvey has spoken at TEDx, is a licensed marriage and family therapist, and earlier in her career worked at the CSU Veterinary Teaching Hospital as a grief counselor and educator.

References

Harvey, Ashley. “An activity that promotes engagement with required readings, even in large classes.” Faculty Focus. February 2017. https://www.facultyfocus.com/articles/effective-teaching-strategies/activity-promotes-engagement-required-readings-even-large-classes/

Harvey, Ashley. “Fired up and ready to discuss.” Faculty Focus. February 2020. https://www.facultyfocus.com/articles/effective-classroom-management/fired-up-and-ready-to-discuss/

Rentz, Jeremy. “Put the hammer down and build your teaching toolbox.” The Best of the 2021 Teaching Professor Conference. October 2021. https://www.magnapubs.com/wp-content/uploads/2021/10/free-report-Best-of-TPC-2021-opt.pdf

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March 10, 2025