Tag: Focus

Introducing Focus Friday | HESA

Hi everyone,

I’m excited to share a new initiative we’re launching this fall: Focus Friday.

Focus Friday is a biweekly webinar series that brings people together around some of the most pressing issues in higher education. Each session runs for an hour and features a few short conversations with invited guests, followed by an open, coffee-chat style discussion where everyone can join in.

The goal is simple: create space to share ideas, meet new people across the sector, and learn from one another. We especially encourage you to join sessions on topics that may be outside your usual focus as sometimes that’s where the most interesting insights emerge.

Over the course of this initiative, we’ll be diving into themes like current enrolment trends, student experience, the future of research, and even integrating elements of our AI-CADEMY programming into the series. If you’re doing something exciting in any of these areas, I’d love to hear from you. There’s a suggestion box built directly into the Zoom registration form, and you can always drop me a line at [email protected].

In each Focus Friday, you’ll receive the Zoom registration link for the next session. For example, below you will find the link to register for our first full session on September 26 from 12:30–1:30pm Eastern.

I’m excited to chat with you!

Cheers,

Tiff

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September 12, 2025
The Urgency for Outreach to Shy College Students – Faculty Focus

Shy students do not come to office hours nor seek the advice of faculty members. In the college classroom, they are quiet and reserved and may appear nervous or lacking confidence. Professors know the keys to success in college and gladly share these with students who ask our advice or visit our office, but assertive outreach is the only way to share critical advice with the shy student. What they do not know can harm them or at least set them back relative to peers, especially worrisome if they are first generation, lower income, or underrepresented minority students. Many outreached hands are needed to support and advise these students who lack the assertiveness to come to us. We must go to them.

In this article, I will review why many students appear shy in the college classroom and the keys to success every college student needs to hear from us in their freshman and sophomore years. Such outreach supports student retention, providing practical advice to help students navigate the college landscape toward successful completion of their degree.

Empathize with Shy Students in the Classroom

Better mentoring begins with empathy, so reflect upon a time when the college classroom was an intimidating place. Shy students are thinking much the same as we did in our first year of college. They can feel insecure and self-conscious, making social comparisons in an environment where grading makes explicit the varying levels of ability. They are inexperienced young adults, unsure of themselves or their competence (Medialle & Usinger, 2019). Consider the psychology of shy students within the unique performance environment of the university.

Class Participation

Shy students are afraid to make mistakes, fearing negative social judgment. Faculty contribute to this anxiety when we grade for class participation in our syllabus, so it is important to balance this practice with gentle encouragement to speak up and engage. Students adopt a risk-avoidance approach to classroom participation and so calculate potential benefits (reward for my effort), odds of success, and risk of not performing well, resulting in their level of effort calculation (B-O-R-E; Hendricson, 2014). Instructors can encourage class participation with verbal praise for positive contributions and affirming words for mistakes. Students’ perceived odds of success and risk can be shaped by our question (e.g., request a recap rather than extrapolation), by our thoughtful lead-up to the question (e.g., Q&A to draw a conclusion), or by offering help when met with silence (“the correct answer starts with a C … anybody?”). Be sensitive to the shy student’s thinking while in the Q&A environment and allow their initial silence to be a moment of calculation before action.

Power Dynamics in the Classroom

Adding to the intimidating nature of the classroom interaction, instructor power is both manifest and latent. We control course grades, write letters of recommendation, and grant access to professional networks. But we can also use latent authority to set a positive classroom tone: Be welcoming to all, learn students’ names and use them, and be happy during class or seem to be (act happy). Avoid teasing, sarcasm, and mockery as forms of humor as these may be poorly received. To shy students, an instructor’s outreached hand reminds them that you are a supportive approachable figure rather than a formidable authority. Be sensitive to the shy student’s perception of the classroom power dynamic and be careful how you wield this power.

Not Knowing the Unwritten Rules of College

Shy students more slowly engage and learn the common behaviors, values, and beliefs gotten only through their interactions with people on campus. Mintz (2024) reviewed college’s “hidden curriculum”, the institutional culture, expected behavior, and unspoken norms that influence academic success. Asking questions in class and help-seeking behavior for writing or tutoring services, although normal for college students, could be a first time experience for some shy students. They may associate stigma or shame with needing help, thinking “I’ll figure it out on my own” or “I should be able to do this”. Chatting informally with professors before or after class can seem awkward, especially if shy students view the professor as too busy, too important, or too different from me to be bothered. Visiting office hours is also common for college students and is an effective icebreaker to view faculty as supportive approachable figures, but it depends fully upon students asserting themselves. While just as important as the academic curriculum, the lessons of college’s hidden curriculum can take longer to reach students who are members of underrepresented groups (Smith, 2015). Not knowing the common rules of conduct can lead to missed opportunities that later become regrets.

Being Different, Blazing New Trails

As implied in several observations above, the new university environment may hold achievement-oriented beliefs, practices, and values that differ dramatically from a new college student’s home and family culture (Pyne & Means, 2013). Self-advocacy can feel selfish, and help-seeking can feel like evidence that college success is beyond one’s current competencies. For the first-generation trailblazers, attending college alone is a major step when they are deviating from family traditions, practices, or expectations (Cerezo, Lyda, Beristianos, Enriquez & Connor, 2013; Martin, Galentino & Townsend, 2014). Be sensitive to the shy student’s imposter syndrome, that intimate feeling that you may not belong here common in first-year students who lack a role model to help them navigate their college years (e.g., Nelson, 2024).

Early Advice that All College Students Need to Hear

Mentors with college experience can empathize with missed opportunities that became regrets. The advice many of us give today likely consists of some of those “what I wish I had known then” lessons. Because shy students do not come to office hours nor seek our advice, there is an urgency for outreach and mentoring. Shy students do not know what they do not know, and what they don’t know now can harm them in the near future. Help shy students, and all students, avoid these four pitfalls.

Protect Your Grade Point Average (GPA)

Students need a high GPA above 3.0 to compete for the better jobs or when applying for post-baccalaureate studies. GPA is a simple numerical indicator of overall abilities compared to other applicants, and it is no surprise that busy employers prefer the demonstrated work ethic of ‘A’ students over that of ‘C’ students. Once broken, a student’s GPA cannot be fixed without retaking courses for a higher grade, a time-consuming and limited option. Grades for courses in the major should be highest, ideally straight As, because this is supposed to be your favorite topic. A GPA below 3.0 weakens your entire application package for post-baccalaureate studies. Those admissions committees also like to see a steady increase in GPA into your junior and senior years, showing improved learning skills. We have seen too many students wait until senior year to decide to pursue graduate or professional school only to discover that a grade point average below 3.0 limits their opportunities.

Get Noticed in the Classroom. Instructors Write References.

Students influence the future content of job references and letters of recommendation by their own actions and words in the presence of faculty letter writers. Too few students are advised to get noticed early in the classroom by their professors, even though most students will need references or recommendations for their first job or for applications to post-baccalaureate study. Faculty write strong letters for students we remember, the ones who stood out in class because they showed enthusiasm and inquisitiveness (Gomez, 2016). Weak letters of recommendation come from professors with little memory of a students’ classroom performance and with no long-term student-faculty relationship. Advise students to participate in class to highlight the academic prowess, personality, and social skills they want future letter writers to remember. Advise students to take only in-person courses with potential future letter writers, because online courses with minimal personal interaction leave a weaker impression on the instructor’s memory. If students sit passively and quietly in class, do not get involved in the major, and do not interact with faculty in memorable ways, it is difficult for faculty to write strong letters of recommendation when called upon. This will be the case even for students who are highly intelligent with good grades, strong test scores, and immense potential.

Refine Essential Skills

Students may not realize the importance of refining essential next-level skills, such as writing scholarly prose, developing spoken communication skills, and mastering the efficient use of library resources. Students need to know that every writing assignment is a chance to improve through thoughtful feedback from an experienced content expert reading your paper. Every oral presentation is your only chance to improve public speaking skills needed in every future career. Every group project is a chance to practice teamwork because future job interviews will ask about experiences where you collaborated effectively with others to achieve a shared goal (Smith, 2023). All students must master searching library resources and consolidating scattered facts into a coherent argument or essay. After the completion of first-year English composition courses, it can be easy for faculty within the major to ignore the importance of essential competencies, but weak communication skills show up later in job applications, in-person interviews, and writing samples.

Find a Mentor to Guide You

Finally, shy students need to know that mentors teach students about college opportunities and how to avoid mistakes. There is no better way to learn about the vast opportunities in a specific professional field than in a one-on-one mentoring relationship with a seasoned professional. Mentors help students find the right degree and ensure that college choices contribute to developing essential career-relevant skills that lead to a thriving career (Pascarella & Terenzini, 2005; Kuh & Hu, 2001). Outreach is especially important for ethnic minority, lower income, and first-generation college students for whom attrition rates are especially worrisome (Keels, 2013; Museus, 2011). For first-generation students in particular, mentors offer the same advice that second-generation college students allegedly benefit from, i.e., the advice college educated parents give to their own college-aged children. Reach out to shy students to help them plan a road map to career success and learn the hidden opportunities available during the college years.

Active Outreach Facilitates Student Development and Supports Retention

Key contributors to college student success include frequency of contact with professors, mentor support, and high expectations (Baker, 2013; Huss, Randall, Patry, Davis & Hansen, 2002; Yorke & Thomas, 2003). Interactions with faculty members are more significant in the early years of college (Schreiner, 2010). It is mentors — faculty, college staff, and even older peers — who shape the student’s developmental process through active relationships that psychologists suggest transform personal identity from high school student to career-ready college graduate (Chickering & Reisser, 1993; Erikson, 1968). College mentors should focus upon career planning in the freshman and sophomore years to facilitate that identity shift needed for undergraduates to envision their future professional selves (Martin, Spolander, Imran & Beulah, 2014; Perez, Cromley & Kaplan, 2013). Interactions with faculty also play a positive role in student persistence (Pascarella & Terenzini, 2005), specifically being supportive of student needs, being approachable, and returning phone calls and emails in a timely fashion (Lundquist, Spalding & Landrum, 2002-2003). Faculty mentors facilitate student development and support retention by encouraging college students to start thinking strategically about their career options and possible futures.

Shy college students may lack the assertiveness to come to us, so we must go to them. Help shy students avoid pitfalls during their college years and teach them the importance of high grades, mentoring relationships, and essential communication skills. More outreached hands in the freshman and sophomore years remind students that faculty are supportive approachable figures rather than formidable authorities. Shy students are inexperienced young adults and need our support to reach their fullest potential.

John Gomez is a professor of psychology in his twenty fifth year of teaching and mentoring undergraduate students at Our Lady of the Lake University, a Hispanic-serving institution located in San Antonio, Texas. His research program and teaching focus upon helping undergraduate students to think strategically about their future professional selves, with special attention to mentees’ development of identity and purpose in life. A research focus has been on ethnic minority, lower income, and first-generation college students, for whom national college retention rates are most troubling. He mentors college students about academic self-preparation, graduate school decision-making, and career planning in psychology and related fields. He also mentors first-time college instructors about classroom management and career readiness. He has delivered peer-reviewed research talks at multiple mentoring conferences, including the Mentoring Institute (University of New Mexico) and the International Mentoring Association (University of Florida).

References

Baker, C. (2013) Social support and success in higher education: The influence of on-campus support. Urban Review, 45(5), 632-650.

Cerezo, A., Lyda, J., Beristianos, M., Enriquez, A. & Connor, M. (2013) Latino men in college: Giving voice to their struggles and triumphs. Psychology of Men and Masculinity, 14(4), 352-362.

Chickering A. & Reisser, L. (1993). Education and identity, 2nd ed. San Francisco: Jossey-Bass.

Erikson, E. (1968). Identity: Youth and crisis. New York, NY: W. W. Norton & Co.

Gomez, J. (2016 Spring). How to properly request letters of recommendation from your professors: Ask, don’t tell. Eye on Psi Chi magazine. American Psychological Association.

Hendricson, W. (2014) Interpreting a Student’s Apparent Lack of Motivation. Academy for Academic Leadership News. 2013; 3(1). DOI: 10.13140/RG.2.1.5097.2644/1

Huss, M., Randall, B., Patry, M., Davis, S. & Hansen, D. (2002). Factors influencing self-rated preparedness for graduate school: A survey of graduate students. Teaching of Psychology, 29(4), 275- 281.

Keels, M. (2013) Getting them enrolled is only half the battle: College success as a function of race or ethnicity, gender, and class, American Journal of Orthopsychiatry, 83(2,3) 310-322.

Kuh, G. & Hu, S. (2001). The effects of student-faculty interactions in the 1990s. Review of Higher Education, 2, 309-332.

Lundquist, C., Spalding, R. & Landrum, E. (2002-2003). College students’ thoughts about leaving the university: The impact of faculty attitudes and behaviors. Journal of College Student Retention, 4, 123- 133.

Martin, K., Galentino, R. & Townsend, L. (2014) Community College Student Success: The role of Motivation and Self-Empowerment, Community College Review, 42(3), 221-241.

Martin, L., Spolander, G., Imran, A. & Beulah, M. (2014) Evolution of student identity: A case of caveat emptor, Journal of Further & Higher Education, 38(2), 200-210.

Medialle, A. & Usinger, J. (2019 March) Engaging quiet students in the college classroom. College Teaching, 67(2):130-137. DOI:10.1080/87567555.2019.1579701.

Mintz, S. (2024 May 16) Life Prep 101. Inside Higher Education online magazine. https://www.insidehighered.com/opinion/blogs/higher-ed-gamma/2024/05/16/how-hidden-curriculum-prepares-students-post-college-life

Museus, S. (2011) Generating ethnic minority student success (GEMS): A qualitative analysis of high-performing institutions, Journal of Diversity in Higher Education, 4(3), 147-162.

Nelson, R. (2024 Winter) Succeeding in college without falling into the imposter phenomenon trap. Eye on Psi Chi magazine. Washington, DC: American Psychological Association.

Pascarella, E. & Terenzini, P. (2005). How college affects students, Vol. 2, A third decade of research. San Francisco, CA: Jossey-Bass.

Pyne, K. & Means, D. (2013) Underrepresented and In/visible: A Hispanic first-generation student’s narratives of college, Journal of Diversity in Higher Education, 6(3), 186-19.

Perez, T., Cromley, J. & Kaplan, A. (2013) The role of identity development, values, and costs in college STEM retention, Journal of Educational Psychology, doi: 10.1037/a0034027.

Schreiner, L. (2010) The critical role of faculty and faculty development in sophomore success. In Hunter, M. S., Tobolowsky, B. F. & Gardner, J. N., (Eds.) Helping sophomores succeed: Understanding and improving the second-year experience. San Francisco, CA: Jossey-Bass.

Smith, A. (2023 April 25) Teamwork is hard work. Here’s how to build the skills to do it well. Forbes online magazine. https://www.forbes.com/sites/annkowalsmith/2023/04/25/teamwork-is-hard-work-heres-how-to-build-the-skills-to-do-it-well/

Smith, B. (2015 March 24) Mentoring at-risk students through the hidden curriculum of higher education. Lexington Books.

Yorke, M. & Thomas, L. (2003). Improving the retention of students from lower socio-economic groups, Journal of Higher Education Policy and Management, 25(1), 63-74.

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September 12, 2025
How To Teach With AI Transparency Statements – Faculty Focus

I remember talking to my students about GenAI for the first time, almost three years ago. We covered the uses and limitations of some tools. We talked about the danger of losing our own voice. We talked about the importance of writing in our own words, even if the end product isn’t as neat as AI-generated prose. I remember taking over the conversation, asserting what GenAI is good for and what it’s bad at. I fell into a pattern of announcing “this is fine” and “don’t do that.”

At the time, it was the best I could do.

Since then, I have spent a lot of time refining my approach to GenAI in the classroom. I realized that my initial approach – though sound at the time – deprived my students of agency. I allowed my students to share their ideas about the technology. But then, I stepped in to make all the decisions that really mattered.

My approach now is very different. I ask my students to design their own writing processes, implement them, and reflect on them. Some of my students work GenAI into the process, while others prefer to leave it alone. I empower my students to decide whether and how GenAI should play a role in their work. At the same time, I want them to be mindful of how easy it is to give our voices away when we use this technology.

The key to making this all work is AI Transparency Statements.

We can use these statements to encourage our students to use GenAI responsibly and to think more deeply about their relationships to this technology, while also building a culture of trust and accountability. They are invaluable.

In the rest of this article, I will cover the two actions I ask students to complete in their AI Transparency Statements. Feel free to replicate and adapt them.

Step 1: Students describe how they used GenAI, according to our AI scale

As mentioned above, my students design multi-step processes for completing their projects. In the first part of the AI Transparency Statement, I ask my students to go back to those steps and self-report if and how they used AI. And depending on the context, I sometimes ask them to submit chat transcripts with their statement.

I refer them to the AI Assessment Scale (AIAS), which was developed by Leon Furze, Mike Perkins, Jasper Roe, and Jason MacVaugh. This scale assigns numbers for levels of GenAI usage, ranging from 1 to 5. A 1 means the student used no GenAI. A 2 means that the student used GenAI for pre-task activities like planning and brainstorming. A 3 means that the student used GenAI for assistance in completing the task itself. For example, some students use GenAI programs to get feedback. Or they use it for rephrasing some sentences or a paragraph. A 4 means that the student used GenAI to complete the task, with their direction. A 5 means that the student is using GenAI to find innovative solutions to a problem and to boost their own creativity.

Often, professors treat the AIAS as a faculty-facing tool. For example, we might tell students to stick to level 2 for an essay outline: they can use a GenAI program for brainstorming and getting started, but we want them to create the outline themselves.

But for me, the real power of the AIAS is as a student-facing tool. We can ask students to give the different parts of their process a rating, and then walk through why they gave them that rating. This encourages responsible AI usage, as well as meta-awareness.

Step 2: Students defend their use or non-use of GenAI

Once students assign AIAS numbers to their process, I then ask them to defend their use or non-use of GenAI. Why did they feel that level-3 usage of GenAI was appropriate for that part in the process? What would have been lost if they went down to a level-2 usage or up to a level-4 usage?

As they defend their use or non-use of GenAI, I want them to think about power and personal voice. Did that specific use of GenAI empower them or did it take their power away?

The goal, here, is to encourage students to link their decisions to larger questions about personal empowerment. After all, the responsible use of AI isn’t just about upholding academic integrity. It’s about making decisions that don’t give away our voice.

Final Thoughts

Using AI Transparency Statements doesn’t AI-proof assignments, if such a thing were even possible. Rather, these are a chance to highlight the importance of self-reflection and intentionality as we interact with these systems — not only in classes, but in our everyday lives.

Without these skills, it’s incredibly easy to lose ourselves.

Jason Gulya is a Professor of English and Communications at Berkeley College, where he chairs the AI and Academic Integrity Committee and serves on the AI Task Force. His first book was titled “Allegory in Enlightenment Britain: Literary Abominations” (Palgrave Macmillan, 2022). He also co-wrote a book titled “Artificial Intelligence, Real Literacy.” (2025) with Paul Matthews. He’s currently researching the rise of GenAI, process-based teaching, and alternative assessment.

Reference

Furze, Leon. “Updating the AI Assessment Scale,” https://leonfurze.com/2024/08/28/updating-the-ai-assessment-scale/

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September 10, 2025

More Than a Name: How Assignment Labels Influence Student Learning and Performance – Faculty Focus

In higher education, assignment titles often serve a functional purpose, indicating the sequence of assignments rather than their specific objectives. However, intentionally structuring assignment titles and formats can significantly influence student engagement and learning outcomes, especially in online courses. Applying adult learning theory, we redesigned assignments to emphasize process-based learning and offer scaffolded opportunities for students to engage with authentic tasks. This approach clarified expectations, enhanced performance, and fostered a more collaborative relationship between faculty and instructional designers.

We grounded our redesign in adult learning theory (andragogy) because our student population skews older, with an average age of 25, and many are returning learners balancing coursework with careers and family responsibilities. Adult learning theory emphasizes that adults learn best when they understand why they are learning something, see clear relevance to real-world contexts, and are given opportunities to reflect and apply their knowledge authentically. With these principles in mind, we aligned assignments with professional scenarios, built feedback opportunities into the workflow, and broke tasks into logical phases to support reflection and critical thinking.

Building on this foundation, we reconsidered how assignments were labeled and structured. Traditionally, assignments were labeled “Homework 1” or “Homework 2.” These labels communicated sequence but not purpose. To foster student understanding and engagement in a Prescriptive Analytics course that focuses on model building and model analysis, we restructured assignments around two key phases—Model Setup and Model Insights—and renamed them using a consistent structure—Model Setup and Model Insights—followed by a number (e.g., Model Setup 3, Model Insights 3). This format clarified each assignment’s stage in the modeling process without overwhelming students with overly complex titles.

Figure 1. The revised prescriptive analytics modeling format and goals emphasize the modeling process and are split into two submissions with interim instructor feedback.

To enhance student learning, we divided the homework assignments into two submissions with an interim instructor assessment. For instance, in one assignment where students had to match project managers to various projects, they first reviewed fictional emails presenting the problem, constraints, and necessary data. After completing their project’s initial setup, students received individualized instructor feedback that encouraged them to think critically about how they approached the problem and the assumptions they made before moving on to analyze their results. This opportunity for early intervention provided support, reduced confusion, and built students’ confidence to better prepare them for creating and running their spreadsheet models. Overall, this two-step approach fostered deeper thinking and engagement with the instructional material and interpreted the outcomes through additional questions and presentations.

To support student learning and prepare them for the graded modeling assignments, we introduced each module with a sequence of low-stakes, formative quizzes built around simplified case-based scenarios using business-style communication such as mock emails and voicemails. These activities helped students build familiarity with core modeling concepts and decision-making logic before tackling more complex tasks.

Graded modeling assignments were framed using business-style communications such as mock emails and memos to reinforce real-world relevance. For example, in Model 4, students received a series of emails outlining a realistic assignment problem, including objectives, constraints, and relevant data. Students submitted their Model 4 Setup individually and received instructor feedback before advancing to the Model 4 Insights phase, where they used Excel to build an optimization model, interpret the results, and respond to scenario-specific questions in a timed quiz. A short video presentation accompanied their submissions to explain their recommendations and rationale. Excel was selected as the modeling platform because it is freely accessible to our students and includes Solver functionality. This two-phase assignment format, supported by structured templates and clearly defined expectations, offered early guidance, encouraged iterative learning, and promoted deeper engagement with course concepts while supporting individual accountability.

The results were clear when comparing course participation before and after implementing the new approach. Submission rates for all modeling assignments increased by 18 percentage points. In addition to higher completion rates, students demonstrated measurable improvements in problem formulation, critical thinking, and communication skills. These changes aligned closely with the course learning outcomes and were reinforced through targeted quiz prompts and structured instructor feedback.

Most notably, engagement in the “new approach course” extended beyond basic requirements. While at least one sensitivity analysis was required, nearly half of the students voluntarily completed an additional, optional analysis. Given how difficult it can be to motivate students to complete even required coursework, this level of voluntary participation highlights the power of purposeful assignment design to drive deeper student investment.

Based on this redesign experience, we recommend several strategies for instructors and instructional designers aiming to create more engaging, authentic assignments. These include using purposeful titles, scaffolding complex tasks, framing assignments in real-world contexts, and integrating tools that support communication and feedback.

Table 1 provides an overview of these strategies and design recommendations:

Strategy	Example	Purpose
Descriptive Titles	“Model Setup 4” “Model Insights 4”	Clarifies the purpose and phase of work
Scaffolding	Two-part submissions with feedback	Reduces cognitive overload, reinforces learning
Real-World Framing	Case-based prompts, mock business emails	Builds relevance and engagement
Technology Integration	Spreadsheet model, video reflection	Supports student modeling practice, professional communication of a problem statement, solution approach, and recommendation with diverse student expression

By renaming assignments, scaffolding tasks, incorporating real-world scenarios, and integrating student-centered feedback mechanisms, we helped students move from performative task completion to meaningful, process-oriented learning. Our collaboration demonstrated that intentional course design, grounded in adult learning principles, can make assignments more than a grade. It can make them matter.

Most importantly, these strategies are highly transferable. While our work was rooted in a prescriptive analytics course, the principles of clear purpose, scaffolded structure, and authentic engagement can be applied across disciplines and modalities. Faculty and instructional designers can adapt these approaches for various disciplines in the humanities, sciences, or professional programs to support meaningful learning.

This redesign also reinforces the value of partnership between instructors and instructional designers. Through open collaboration, we combined disciplinary expertise with design thinking to create a learning experience that was more intentional, more engaging, and ultimately, more effective.

Salina Randall, MEd, is the Associate Director of the Center for Teaching, Learning, and Technology at the University of West Florida. She leads initiatives focused on instructional design, faculty development, and digital accessibility. With over a decade of experience in higher education, she supports faculty in designing inclusive, engaging, and data-informed learning experiences. Her work spans course design, accessibility compliance, and strategic communication, with a particular emphasis on empowering faculty through collaboration and practical solutions.

Dr. Julie Ann Stuart Williams is a Professor of Business Administration at the University of West Florida. Her research spans operations management and business education, with recent work focusing on improving business student writing and modeling-based learning. She has co-authored papers with students in journals such as the European Journal of Operational Research and Business and Professional Communication Quarterly. A recipient of UWF’s Faculty Excellence in Teaching Award, Dr. Williams also serves on the editorial board of INFORMS Transactions on Education and as 2nd Vice Chair of the INFORMS Committee on Teaching and Learning. She holds a Ph.D. in Industrial and Systems Engineering from Georgia Tech.

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September 8, 2025

From Policing to Pedagogy: Navigating AI’s Transformative Power – Faculty Focus

The rapid integration of generative artificial intelligence (GenAI) into higher education has reignited a familiar moral panic around academic dishonesty. While much of the immediate institutional response has centered on detection and enforcement, this reaction echoes a familiar pattern—one that treats academic dishonesty as an individual moral failure rather than a symptom of broader systemic issues (Bertram Gallant, 2008). Students, faced with overloaded schedules, mounting debt, and an unforgiving emphasis on grades, often make pragmatic decisions about where to invest their time and effort gravitating toward efficiency and certainty over exploration and risk. In this environment, the ethical clarity around “cheating” becomes murky—not because students don’t understand right from wrong, but because the structures around them reward efficiency and penalize risk. AI tools such as ChatGPT or Grammarly simply fit into this equation as time-saving devices—much like calculators, Google, or even essay mills before them. What has shifted is not the motivation, but the accessibility. A student’s use of AI is a reflection of the transactional nature of how learning is often experienced in contemporary institutions.

The proliferation of AI tools has not so much introduced a new problem as it has exposed the fragility of existing educational structures. Before we focus on what AI changes, we must confront what it reveals: that higher education has long incentivized a culture of performance over learning. Research has shown that when students perceive academic work as irrelevant, overly procedural, or divorced from their goals and identities, they disengage—regardless of whether AI is available (Ambrose et al., 2010). In this light, the question is not “How do we catch them?” but rather, “What kind of learning environments have we built that make outsourcing feel logical or even necessary?” This reframing shifts the pedagogical conversation from one of compliance and control to one of capacity-building and curiosity. If we can use this moment to ask how to foster a culture of learning that values growth over grades, process over product, and understanding over output, then AI may serve not as a threat to education but as a catalyst for its reinvention.

Why Students Turn to AI

When students turn to generative AI to complete assignments, they are not necessarily demonstrating a lack of understanding or unethical behavior—they are often responding rationally to the conditions of their academic lives. In surveys and interviews, students routinely cite overwhelming workloads, unclear expectations, time scarcity, and mental health struggles as reasons they seek shortcuts or support tools (McCabe, Treviño, & Butterfield, 2001; Pascoe, Hetrick, & Parker, 2020). AI fits seamlessly into a landscape where efficiency is often more highly rewarded than curiosity. Faced with tasks that feel repetitive, decontextualized, or performative, many students learn that their survival depends on doing just enough, not necessarily doing it deeply.

When a student uses AI to write a discussion post or summarize an article, the act is not just a form of evasion—it’s often a judgment about the value of the task itself. If the assignment doesn’t ask them to do something personally meaningful, intellectually stimulating, or clearly useful for their future goals, it becomes an obligation to manage rather than a learning opportunity to embrace. This disconnect is further amplified by the unspoken norms that teach students to prioritize grades, speed, and performance over exploration, failure, and growth (Margolis, 2001). In such a climate, using AI can feel less like cheating and more like optimization. Students aren’t necessarily trying to game the system—they’re playing the game as they perceive it was designed.

The challenge, then, is not to eliminate AI from the learning process, but to design learning experiences that make authentic engagement the easier, more meaningful choice. Rather than dismissing AI use as evidence of student laziness or misconduct, educators might ask: What does the pattern of AI usage tell us about how students experience our courses? What if the real issue isn’t student behavior but assignment design and instructional culture? By attending to these questions, we shift the focus from enforcement to empathy—from trying to control student actions to trying to understand their motivations and constraints.

Process Over Product

If generative AI makes it easier for students to produce work without deeply engaging in the learning process, it invites faculty to reconsider what their assessments are really measuring. Too often, traditional assessments prioritize polished outputs—essays, quizzes, presentations—over the messy, iterative, and uncertain processes that lead to genuine understanding. These conventional formats may reward conformity and correctness, but they do little to cultivate the flexibility, reflection, and critical thinking required in a world shaped by automation and ambiguity.

In contrast, AI offers a lens through which educators can reassess their learning goals: Are students being asked to think or to comply? To demonstrate mastery or to demonstrate growth? Increasingly, the competencies that matter most in both life and work—such as collaboration, problem framing, ethical reasoning, and the ability to adapt—are not easily captured through static, time-bound tasks (National Academies of Sciences, Engineering, and Medicine, 2018). Nor are they easily outsourced to AI. These are precisely the skills that thrive when assessment shifts from evaluating finished products to examining students’ thinking and decision-making processes along the way.

Emerging research on assessment design suggests that when students are asked to reflect on how they used AI, evaluate its limitations, compare its outputs to their own, or build on AI-generated responses with original insights, they begin to see these tools not as shortcuts but as thought partners (Mollick & Mollick, 2023; Popenici & Kerr, 2017). This approach not only fosters academic integrity, but also helps students develop the judgment, discernment, and contextual understanding that AI cannot replicate. Moreover, process-oriented assessments are more inclusive. They allow students with diverse learning styles, linguistic backgrounds, and levels of prior knowledge to demonstrate learning in multiple ways. They also encourage metacognition—students thinking about their own thinking—which is known to enhance long-term retention and transfer of learning (Ambrose et al., 2010). Rethinking assessment does not mean lowering standards; it means realigning them with the kinds of learning we claim to value. When assessments emphasize authentic engagement over mere output, they become more resilient to automation—and more relevant to the lives students are preparing to lead.

Disciplinary Innovation

While discussions about AI in education often remain abstract or policy-focused, meaningful change is already taking root within disciplines as faculty reimagine how to integrate AI into the logic of their fields. Rather than banning its use outright, many instructors are embedding AI into course design in ways that mirror authentic disciplinary practices. The goal is not just to accommodate new tools, but to cultivate students’ judgment in using them—aligning learning with the actual thinking, creating, and problem-solving expected in professional contexts.

Creative Disciplines

In design, media production, and the arts, instructors are moving beyond static portfolio reviews and embracing reflective, process-based documentation. Students are asked to keep iterative journals that detail how their ideas evolved, what role AI tools played in generating or refining concepts, and how creative decisions were made at each stage. This encourages fluency with new tools while reinforcing core disciplinary values: originality, intentionality, and critique. As Sullivan (2010) and McArthur & White (2021) argue, creative practice is inherently dialogic, and integrating AI into that dialogue allows students to develop their own voices in conversation with machine-generated suggestions.

Simulating Complexity

In fields such as public administration, business, and marketing, instructors are shifting from case-study write-ups to scenario-based simulations and role-playing exercises. These assessments ask students to evaluate data, propose interventions, justify decisions, and communicate across stakeholder perspectives. AI-generated content may be introduced as one data source among many, prompting students to critique its assumptions, identify potential biases, or integrate it with qualitative insights. This simulates real-world complexity and ethical ambiguity—core aspects of professional judgment that cannot be easily automated (Farrell, 2023; Wiggins, 2016).

Modeling Scientific Reasoning

In the sciences, instructors are piloting assignments that blend AI-assisted analysis with human critique. For example, students might be given an AI-generated interpretation of experimental data and asked to assess its validity, identify methodological flaws, or propose alternative explanations. These tasks promote skills fundamental to scientific reasoning: skepticism, data literacy, and peer review. As Wieman (2017) and Holmes, Wieman, & Bonn (2015) have shown, such approaches encourage deeper engagement with the epistemological foundations of science—what counts as evidence, how claims are justified, and why precision matters.

Building AI Fluency Across the Curriculum

To meet the demands of a world increasingly shaped by automation, higher education must move beyond treating AI as an external threat and begin cultivating AI fluency as a foundational component of 21st-century learning. AI fluency goes beyond knowing how to use tools like ChatGPT or Midjourney—it encompasses the ability to understand how AI systems work, critically evaluate their outputs, use them ethically, and reflect on their broader social and disciplinary implications (Long & Magerko, 2020; Luckin et al., 2016).

Integrating AI fluency into the curriculum means designing assignments that treat AI not as a banned shortcut or unexamined assistant, but as a subject of inquiry. Students should be taught to interrogate the design of AI tools, including how they are trained, what kinds of data they rely on, and how they reflect or reinforce existing social biases. Courses across disciplines can include assignments that ask students to compare AI-generated outputs with human-created work, evaluate their quality and assumptions, or explore how different prompts lead to different results. These tasks cultivate what Holmes, Bialik, & Fadel (2019) describe as “cognitive partnership”—the ability to think with, through, and about technology, rather than simply accepting its outputs.

Building AI fluency involves normalizing reflective practice. Students should be encouraged to articulate how and why they used AI tools in the completion of an assignment: What choices did they make? What did the AI get right—or wrong? How did it shape their thinking? This kind of metacognitive work not only deepens learning, but also demystifies AI as a black box and empowers students to use it responsibly (Mollick & Mollick, 2023).

Crucially, AI fluency should not be siloed within STEM or computer science. Every field—from history to healthcare, literature to law—will be transformed by AI in some way, and students in every discipline deserve the opportunity to engage critically with those transformations. Developing AI fluency as a form of literacy positions students to become not just competent users of technology, but thoughtful citizens capable of shaping its role in society.

The task before educators is not merely to regulate AI use, but to reimagine what it means to be educated in an age of intelligent machines. By embedding AI fluency into course outcomes, assessment practices, and institutional values, higher education can help students navigate this new landscape with agency, insight, and integrity.

The text was generated in collaboration with Gemini (2.5 Flash), Google’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.

References

Ambrose, Susan A., Michael W. Bridges, Michele DiPietro, Marsha C. Lovett, and Marie K. Norman. How Learning Works: Seven Research-Based Principles for Smart Teaching. San Francisco: Jossey-Bass, 2010.

Bertram Gallant, Tricia. Academic Integrity in the Twenty-First Century: A Teaching and Learning Imperative. San Francisco: Jossey-Bass, 2008.

Farrell, Paul. “Integrating AI into Public Administration Education: Risks and Rewards.” Journal of Public Affairs Education 29, no. 1 (2023): 45–58.

Holmes, Wayne, Maya Bialik, and Charles Fadel. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign, 2019.

Holmes, N. G., Carl E. Wieman, and Doug A. Bonn. “Teaching Critical Thinking.” Proceedings of the National Academy of Sciences 112, no. 36 (2015): 11199–204.

Long, David, and Brian Magerko. “What Is AI Literacy? Competencies and Design Considerations.” In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. New York: ACM, 2020.

Luckin, Rose, Wayne Holmes, Mark Griffiths, and Laurie B. Forcier. Intelligence Unleashed: An Argument for AI in Education. London: Pearson, 2016.

Margolis, Eric. The Hidden Curriculum in Higher Education. New York: Routledge, 2001.

McArthur, Janine A., and Betty White. “Redesigning Creativity: AI, Agency, and Reflective Practice in the Visual Arts.” International Journal of Art & Design Education 40, no. 2 (2021): 437–51.

McCabe, Donald L., Linda Klebe Treviño, and Kenneth D. Butterfield. “Cheating in Academic Institutions: A Decade of Research.” Ethics & Behavior 11, no. 3 (2001): 219–32.

Mollick, Ethan, and Lilach Mollick. “Assigning AI: Seven Approaches for Students with Prompts.” Working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321 (accessed July 8, 2025).

National Academies of Sciences, Engineering, and Medicine. How People Learn II: Learners, Contexts, and Cultures. Washington, D.C.: The National Academies Press, 2018.

Pascoe, Michaela C., Rosanna M. Hetrick, and Alexandra G. Parker. “The Impact of Stress on Students in Secondary School and Higher Education.” International Journal of Adolescence and Youth 25, no. 1 (2020): 104–12.

Popenici, Stefan C., and Sharon Kerr. “Exploring the Impact of Artificial Intelligence on Teaching and Learning in Higher Education.” Research and Practice in Technology Enhanced Learning 12, no. 1 (2017): 1–13.

Sullivan, Graeme. Art Practice as Research: Inquiry in the Visual Arts. Thousand Oaks, CA: SAGE Publications, 2010.

Wieman, Carl E. “The Similarities between Research in Education and Research in the Hard Sciences.” Educational Researcher 46, no. 6 (2017): 319–29.

Wiggins, Grant. “A True Test: Toward More Authentic and Equitable Assessment.” Educational Leadership 70, no. 6 (2016): 28–33.

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September 5, 2025
From Policing to Pedagogy: Navigating AI’s Transformative Power – Faculty Focus

The rapid integration of generative artificial intelligence (GenAI) into higher education has reignited a familiar moral panic around academic dishonesty. While much of the immediate institutional response has centered on detection and enforcement, this reaction echoes a familiar pattern—one that treats academic dishonesty as an individual moral failure rather than a symptom of broader systemic issues (Bertram Gallant, 2008). Students, faced with overloaded schedules, mounting debt, and an unforgiving emphasis on grades, often make pragmatic decisions about where to invest their time and effort gravitating toward efficiency and certainty over exploration and risk. In this environment, the ethical clarity around “cheating” becomes murky—not because students don’t understand right from wrong, but because the structures around them reward efficiency and penalize risk. AI tools such as ChatGPT or Grammarly simply fit into this equation as time-saving devices—much like calculators, Google, or even essay mills before them. What has shifted is not the motivation, but the accessibility. A student’s use of AI is a reflection of the transactional nature of how learning is often experienced in contemporary institutions.

The proliferation of AI tools has not so much introduced a new problem as it has exposed the fragility of existing educational structures. Before we focus on what AI changes, we must confront what it reveals: that higher education has long incentivized a culture of performance over learning. Research has shown that when students perceive academic work as irrelevant, overly procedural, or divorced from their goals and identities, they disengage—regardless of whether AI is available (Ambrose et al., 2010). In this light, the question is not “How do we catch them?” but rather, “What kind of learning environments have we built that make outsourcing feel logical or even necessary?” This reframing shifts the pedagogical conversation from one of compliance and control to one of capacity-building and curiosity. If we can use this moment to ask how to foster a culture of learning that values growth over grades, process over product, and understanding over output, then AI may serve not as a threat to education but as a catalyst for its reinvention.

Why Students Turn to AI

When students turn to generative AI to complete assignments, they are not necessarily demonstrating a lack of understanding or unethical behavior—they are often responding rationally to the conditions of their academic lives. In surveys and interviews, students routinely cite overwhelming workloads, unclear expectations, time scarcity, and mental health struggles as reasons they seek shortcuts or support tools (McCabe, Treviño, & Butterfield, 2001; Pascoe, Hetrick, & Parker, 2020). AI fits seamlessly into a landscape where efficiency is often more highly rewarded than curiosity. Faced with tasks that feel repetitive, decontextualized, or performative, many students learn that their survival depends on doing just enough, not necessarily doing it deeply.

When a student uses AI to write a discussion post or summarize an article, the act is not just a form of evasion—it’s often a judgment about the value of the task itself. If the assignment doesn’t ask them to do something personally meaningful, intellectually stimulating, or clearly useful for their future goals, it becomes an obligation to manage rather than a learning opportunity to embrace. This disconnect is further amplified by the unspoken norms that teach students to prioritize grades, speed, and performance over exploration, failure, and growth (Margolis, 2001). In such a climate, using AI can feel less like cheating and more like optimization. Students aren’t necessarily trying to game the system—they’re playing the game as they perceive it was designed.

The challenge, then, is not to eliminate AI from the learning process, but to design learning experiences that make authentic engagement the easier, more meaningful choice. Rather than dismissing AI use as evidence of student laziness or misconduct, educators might ask: What does the pattern of AI usage tell us about how students experience our courses? What if the real issue isn’t student behavior but assignment design and instructional culture? By attending to these questions, we shift the focus from enforcement to empathy—from trying to control student actions to trying to understand their motivations and constraints.

Process Over Product

If generative AI makes it easier for students to produce work without deeply engaging in the learning process, it invites faculty to reconsider what their assessments are really measuring. Too often, traditional assessments prioritize polished outputs—essays, quizzes, presentations—over the messy, iterative, and uncertain processes that lead to genuine understanding. These conventional formats may reward conformity and correctness, but they do little to cultivate the flexibility, reflection, and critical thinking required in a world shaped by automation and ambiguity.

In contrast, AI offers a lens through which educators can reassess their learning goals: Are students being asked to think or to comply? To demonstrate mastery or to demonstrate growth? Increasingly, the competencies that matter most in both life and work—such as collaboration, problem framing, ethical reasoning, and the ability to adapt—are not easily captured through static, time-bound tasks (National Academies of Sciences, Engineering, and Medicine, 2018). Nor are they easily outsourced to AI. These are precisely the skills that thrive when assessment shifts from evaluating finished products to examining students’ thinking and decision-making processes along the way.

Emerging research on assessment design suggests that when students are asked to reflect on how they used AI, evaluate its limitations, compare its outputs to their own, or build on AI-generated responses with original insights, they begin to see these tools not as shortcuts but as thought partners (Mollick & Mollick, 2023; Popenici & Kerr, 2017). This approach not only fosters academic integrity, but also helps students develop the judgment, discernment, and contextual understanding that AI cannot replicate. Moreover, process-oriented assessments are more inclusive. They allow students with diverse learning styles, linguistic backgrounds, and levels of prior knowledge to demonstrate learning in multiple ways. They also encourage metacognition—students thinking about their own thinking—which is known to enhance long-term retention and transfer of learning (Ambrose et al., 2010). Rethinking assessment does not mean lowering standards; it means realigning them with the kinds of learning we claim to value. When assessments emphasize authentic engagement over mere output, they become more resilient to automation—and more relevant to the lives students are preparing to lead.

Disciplinary Innovation

While discussions about AI in education often remain abstract or policy-focused, meaningful change is already taking root within disciplines as faculty reimagine how to integrate AI into the logic of their fields. Rather than banning its use outright, many instructors are embedding AI into course design in ways that mirror authentic disciplinary practices. The goal is not just to accommodate new tools, but to cultivate students’ judgment in using them—aligning learning with the actual thinking, creating, and problem-solving expected in professional contexts.

Creative Disciplines

In design, media production, and the arts, instructors are moving beyond static portfolio reviews and embracing reflective, process-based documentation. Students are asked to keep iterative journals that detail how their ideas evolved, what role AI tools played in generating or refining concepts, and how creative decisions were made at each stage. This encourages fluency with new tools while reinforcing core disciplinary values: originality, intentionality, and critique. As Sullivan (2010) and McArthur & White (2021) argue, creative practice is inherently dialogic, and integrating AI into that dialogue allows students to develop their own voices in conversation with machine-generated suggestions.

Simulating Complexity

In fields such as public administration, business, and marketing, instructors are shifting from case-study write-ups to scenario-based simulations and role-playing exercises. These assessments ask students to evaluate data, propose interventions, justify decisions, and communicate across stakeholder perspectives. AI-generated content may be introduced as one data source among many, prompting students to critique its assumptions, identify potential biases, or integrate it with qualitative insights. This simulates real-world complexity and ethical ambiguity—core aspects of professional judgment that cannot be easily automated (Farrell, 2023; Wiggins, 2016).

Modeling Scientific Reasoning

In the sciences, instructors are piloting assignments that blend AI-assisted analysis with human critique. For example, students might be given an AI-generated interpretation of experimental data and asked to assess its validity, identify methodological flaws, or propose alternative explanations. These tasks promote skills fundamental to scientific reasoning: skepticism, data literacy, and peer review. As Wieman (2017) and Holmes, Wieman, & Bonn (2015) have shown, such approaches encourage deeper engagement with the epistemological foundations of science—what counts as evidence, how claims are justified, and why precision matters.

Building AI Fluency Across the Curriculum

To meet the demands of a world increasingly shaped by automation, higher education must move beyond treating AI as an external threat and begin cultivating AI fluency as a foundational component of 21st-century learning. AI fluency goes beyond knowing how to use tools like ChatGPT or Midjourney—it encompasses the ability to understand how AI systems work, critically evaluate their outputs, use them ethically, and reflect on their broader social and disciplinary implications (Long & Magerko, 2020; Luckin et al., 2016).

Integrating AI fluency into the curriculum means designing assignments that treat AI not as a banned shortcut or unexamined assistant, but as a subject of inquiry. Students should be taught to interrogate the design of AI tools, including how they are trained, what kinds of data they rely on, and how they reflect or reinforce existing social biases. Courses across disciplines can include assignments that ask students to compare AI-generated outputs with human-created work, evaluate their quality and assumptions, or explore how different prompts lead to different results. These tasks cultivate what Holmes, Bialik, & Fadel (2019) describe as “cognitive partnership”—the ability to think with, through, and about technology, rather than simply accepting its outputs.

Building AI fluency involves normalizing reflective practice. Students should be encouraged to articulate how and why they used AI tools in the completion of an assignment: What choices did they make? What did the AI get right—or wrong? How did it shape their thinking? This kind of metacognitive work not only deepens learning, but also demystifies AI as a black box and empowers students to use it responsibly (Mollick & Mollick, 2023).

Crucially, AI fluency should not be siloed within STEM or computer science. Every field—from history to healthcare, literature to law—will be transformed by AI in some way, and students in every discipline deserve the opportunity to engage critically with those transformations. Developing AI fluency as a form of literacy positions students to become not just competent users of technology, but thoughtful citizens capable of shaping its role in society.

The task before educators is not merely to regulate AI use, but to reimagine what it means to be educated in an age of intelligent machines. By embedding AI fluency into course outcomes, assessment practices, and institutional values, higher education can help students navigate this new landscape with agency, insight, and integrity.

The text was generated in collaboration with Gemini (2.5 Flash), Google’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.

References

Ambrose, Susan A., Michael W. Bridges, Michele DiPietro, Marsha C. Lovett, and Marie K. Norman. How Learning Works: Seven Research-Based Principles for Smart Teaching. San Francisco: Jossey-Bass, 2010.

Bertram Gallant, Tricia. Academic Integrity in the Twenty-First Century: A Teaching and Learning Imperative. San Francisco: Jossey-Bass, 2008.

Farrell, Paul. “Integrating AI into Public Administration Education: Risks and Rewards.” Journal of Public Affairs Education 29, no. 1 (2023): 45–58.

Holmes, Wayne, Maya Bialik, and Charles Fadel. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign, 2019.

Holmes, N. G., Carl E. Wieman, and Doug A. Bonn. “Teaching Critical Thinking.” Proceedings of the National Academy of Sciences 112, no. 36 (2015): 11199–204.

Long, David, and Brian Magerko. “What Is AI Literacy? Competencies and Design Considerations.” In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. New York: ACM, 2020.

Luckin, Rose, Wayne Holmes, Mark Griffiths, and Laurie B. Forcier. Intelligence Unleashed: An Argument for AI in Education. London: Pearson, 2016.

Margolis, Eric. The Hidden Curriculum in Higher Education. New York: Routledge, 2001.

McArthur, Janine A., and Betty White. “Redesigning Creativity: AI, Agency, and Reflective Practice in the Visual Arts.” International Journal of Art & Design Education 40, no. 2 (2021): 437–51.

McCabe, Donald L., Linda Klebe Treviño, and Kenneth D. Butterfield. “Cheating in Academic Institutions: A Decade of Research.” Ethics & Behavior 11, no. 3 (2001): 219–32.

Mollick, Ethan, and Lilach Mollick. “Assigning AI: Seven Approaches for Students with Prompts.” Working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321 (accessed July 8, 2025).

National Academies of Sciences, Engineering, and Medicine. How People Learn II: Learners, Contexts, and Cultures. Washington, D.C.: The National Academies Press, 2018.

Pascoe, Michaela C., Rosanna M. Hetrick, and Alexandra G. Parker. “The Impact of Stress on Students in Secondary School and Higher Education.” International Journal of Adolescence and Youth 25, no. 1 (2020): 104–12.

Popenici, Stefan C., and Sharon Kerr. “Exploring the Impact of Artificial Intelligence on Teaching and Learning in Higher Education.” Research and Practice in Technology Enhanced Learning 12, no. 1 (2017): 1–13.

Sullivan, Graeme. Art Practice as Research: Inquiry in the Visual Arts. Thousand Oaks, CA: SAGE Publications, 2010.

Wieman, Carl E. “The Similarities between Research in Education and Research in the Hard Sciences.” Educational Researcher 46, no. 6 (2017): 319–29.

Wiggins, Grant. “A True Test: Toward More Authentic and Equitable Assessment.” Educational Leadership 70, no. 6 (2016): 28–33.

Source link

September 5, 2025

Design Smarter, Teach Better: How Thoughtful Course Webpages Can Improve Online Learning – Faculty Focus

Even as in-person classes return post-pandemic, online courses haven’t gone away. In fact, many students still opt for online learning because of the flexibility it offers. But one thing is clear: not all online courses are created equal, and one of the biggest differences lies in something many instructors overlook: the course webpage.

Whether you’re using Brightspace, Canvas, or Moodle, how you design your course webpage can make or break your students’ experience. Based on our systematic review of recent studies on online learning and student satisfaction, here’s what we learned—and how you can apply it in your own teaching.

Why Course Webpages Matter More Than You Think

Students form impressions about your course page in less than a second (Lindgaard et al., 2006). If it’s cluttered, confusing, or bland, you might lose them before they even start the first lecture.

We reviewed research involving over 1,600 university students from seven countries. Across the board, students reported that a course webpage’s ease of use, usefulness, and visual appeal significantly influenced their satisfaction and engagement (Lazard & King, 2020; Younas et al. 2021).

How We Did the Research

To better understand what works in online course webpage design, we conducted a systematic review following PRISMA guidelines. Here’s what that looked like:

Databases searched: LearnTechLib, Omni (Carleton University), and reference lists from relevant papers

Keywords: “COVID,” “website,” “online learning,” and “pedagogy”

Inclusion criteria: Peer-reviewed, quantitative studies from 2020–2023, focused on higher education

Total included: 7 studies, 1,614 participants across Australia, China, South Africa, Lebanon, Pakistan, Malaysia, and the U.S.

Figure 1. PRISMA Flow Diagram for the Course Webpage Design Search

Practical Tips for Smarter Course Design

Below are research-based, practical tips instructors can use—that don’t require you to be a web designer. These ideas are easy to implement and can make a big difference.

1. Make Navigation a No-Brainer

Think of your course webpage like your storefront. If people can’t find what they need, they won’t stick around. This makes your life as an instructor harder: you will get more students with last-minute requests for accommodations, as well as students struggling with procrastination.

Use a consistent layout, organize materials by weeks or modules, and give each section clear, descriptive titles. Students should never have to hunt for a syllabus or lecture slides (Bachman & Stewart, 2011; Plous, 2000). I found that having a hyperlink directly on the first page of the course website, helped reduce the amount of student emails on syllabus-related questions (a joy).

Pro tip: Use drop-down menus and collapsible folders to reduce visual clutter.

2. Keep It Clean and Simple

Visual overload is real. Too many colours, clashing fonts, or random clipart can be overwhelming. Stick to a minimalist design with just enough contrast and white space to guide the eye. Use consistent font styles and colours to help students focus (Lazard & King, 2020).

Pro-tip: Avoid red text on black backgrounds—students with colour vision deficiencies may struggle with this combo. The Science Communication Resource Corner provides helpful Colorblind Safe Colour Schemes: https://www.nceas.ucsb.edu/sites/default/files/2022-06/Colorblind%20Safe%20Color%20Schemes.pdf

3. Offer Customization Options

Students reported feeling more satisfied when they could personalize their learning environment—for example, setting their own notification preferences or receiving automatic updates about grades and deadlines (Younas et al., 2021). If your platform allows it, show students how to use these features. Most announcement tools allows you to include the student’s name in the communication by using the code: {firstname} (e.g., Brightspace: https://community.d2l.com/brightspace/kb/articles/6105-automatically-customize-course-content-using-replace-strings). I cannot count the number of students who said they appreciated this customization of information.

Protip: Include a quick walkthrough video or FAQ page on how to customize settings.

4. Build Trust Through Transparency

Technology glitches can tank your credibility fast. Provide assessment submission confirmations, test your hyperlinks, and use clear, timely communication to reduce student anxiety (Hsu et al., 2022).

Protip: Create a “Tech Check” page with test links and troubleshooting steps.

From Theory to Practice: A Usability Framework

Our review supports the Technology Acceptance Model (TAM): a course site is more likely to be used—and liked—if students find it both easy to use and useful (Davis, 1989).

Figure 2. Adapted from “The Technology Acceptance Model (TAM) and its Application to the Utilization of Mobile Learning Technologies,” D. G. Mugo, K. Njagi, B. Chemwei, and J. O. Motanya, 2017, British Journal of Mathematics & Computer Science, 20, p. 4 (DOI: 10.9734/BJMCS/2017/29015). In the public domain.

But we also found that students valued more than just functionality. Our systematic review supported features like customization, organization, and aesthetic appeal as beneficial to student use of the course website (which contributed to higher grades and completion rates).

Quick Design Checklist

Feature	Example	Research Support
Clear navigation	Weekly modules, labeled folders	Bachman & Stewart, 2011
Aesthetic simplicity	Balanced colour palette, consistent fonts	Lazard & King, 2020
Customization	Notification settings, adaptable layout	Younas et al., 2021
Confirmation of actions	Email receipt of submissions	Hsu et al., 2022
Mobile compatibility	Testing on phones and tablets	Plous, 2000

Collaborate with Your Students

Want the best feedback on your course webpage? Ask your students. Mid-semester feedback surveys or co-design sessions can go a long way in improving usability (Yoshida & Thammetar, 2021). I have benefited from undergraduate student input, and these translated to higher ratings for my courses.

Bottom Line: Thoughtful Design = Better Learning

Course webpage design isn’t just about looking polished—our systematic review suggests it’s a key factor in student satisfaction and learning success. Students are more likely to engage when the site is:

Adaptable to their needs

Regularly maintained and updated

So we, as instructors, need to spend some time fine-tuning our websites. The good news is that once you do this for one course, many platforms offer cloning or importing, which makes it easy to transfer your work from one course to another.

If you’re feeling stuck, reach out to your campus teaching and learning centre, look at exemplars, or co-create the website with students. You don’t have to do it alone.

The full systematic review can be found: https://osf.io/j6xc8/?view_only=bf720f88103848578ba9e6da0013f95f

Dr. Kelly M. Babchishin is an assistant professor at Carleton University who specializes in forensic psychology. She teaches large undergraduate and graduate courses, many of which use online or hybrid formats.

Emma J. Holmes is a graduate student and teaching assistant at Carleton University (Department of Psychology). Emma is supported in part by funding from the Social Sciences and Humanities Research Council and the Ontario Graduate Scholarship.

Alexis G. Hinkson was an undergraduate student (Department of Psychology) at Carleton University and is now completing her law degree at the University of Ottawa. Alexis has served as a teaching assistant and was hired by Kelly Babchishin to assist her in course design and provided the student perspective.

References

Bachman, C. M., and C. Stewart. 2011. “Self-Determination Theory and Web-Enhanced Course Template Development.” Teaching of Psychology 38 (3): 180–88. https://doi.org/10.1177/0098628311411798.

Davis, F. D. 1989. “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology.” MIS Quarterly 13 (3): 319–40. https://doi.org/10.2307/249008.

Hsu, P. S., E. M. Lee, and T. J. Smith. 2022. “First-Year Instructor’s Designing and Teaching an Online Undergraduate Engineering Course during the COVID-19 Epidemic.” Journal of Computers in Mathematics and Science Teaching 41 (3): 215–43.

Lazard, A. J., and A. J. King. 2020. “Objective Design to Subjective Evaluations: Connecting Visual Complexity to Aesthetic and Usability Assessments.” International Journal of Human-Computer Interaction 36 (1): 95–104. https://doi.org/10.1080/10447318.2019.1606976.

Lindgaard, G., G. Fernandes, C. Dudek, and J. Brown. 2006. “Attention Web Designers: You Have 50 Milliseconds to Make a Good First Impression.” Behaviour & Information Technology 25 (2): 115–26. https://doi.org/10.1080/01449290500330448.

Plous, S. 2000. “Tips on Creating and Maintaining an Educational World Wide Web Site.” Teaching of Psychology 27 (1): 63–70. https://doi.org/10.1207/S15328023TOP2701_13.

Younas, A., C. M. N. Faisal, M. A. Habib, R. Ashraf, and M. Ahmad. 2021. “Role of Design Attributes to Determine the Intention to Use Online Learning via Cognitive Beliefs.” IEEE Access 9: 94181–94202. https://doi.org/10.1109/ACCESS.2021.3093348.

Yoshida, M., and T. Thammetar. 2021. “Education between GovTech and Civic Tech.” International Journal of Emerging Technologies in Learning 16 (4): 52–68. https://doi.org/10.3991/ijet.v16i04.18769.

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September 3, 2025

Serving to Lead: The Transformative Role of Servant Leadership in Higher Education – Faculty Focus

Students learn just as much from who we are as educators as they do from what we teach. While content is important, the way faculty members carry themselves, how they communicate, lead, and treat others, can leave an even deeper impact on students’ personal and professional growth. This article explains that educators who use servant leadership principles create trust, empathy, and ethical development in students, ultimately supporting both academic success and long-term character building of students.

Throughout this article, I will explain why servant leadership is especially relevant in higher education today. While using a student-centered philosophy is very beneficial, adopting a servant leadership approach offers an extension of that philosophy. This is not just about teaching content effectively. It is about modeling values like integrity, empathy, and thoughtful decision making in ways students can absorb and carry with them beyond the classroom.
In this paper, I will provide research and real-world examples. I will present practical strategies faculty members where they know their students are walking away a better individual, professionally and personally. I will also discuss how faculty members themselves benefit from adopting a servant leadership model, finding stronger connections with students and a deeper sense of purpose in their work.

Modeling Values in the Classroom

One of the clearest ways servant leadership shows up in higher education is through the modeling of values that students can see and experience in everyday interactions. As Rabin and Smith say, “students learn as much from who we are as what we teach” (p. 2, 2013). This speaks to the purpose of servant leadership in higher education. In the context of the classroom, faculty who embody servant leadership not only communicate course content but also demonstrate values such as compassion and care through their actions.

This leadership approach directly supports student-centered teaching by emphasizing the importance of understanding students’ backgrounds, life circumstances, and unique learning needs. In the research article, Rabin and Smith explain that “to care for students, a teacher needs to know her students well enough to understand their unique motivations and needs” (p. 4). This describes a strong part of servant leadership, placing the needs of others before one’s own and creating relationships built on trust and mutual respect.

When higher education faculty take on the role of servant leaders, they transform learning environments. They challenge the authority-driven classroom stereotype and replace it with a relational one, where students are empowered, respected, and mentored not only academically, but as people. These professors serve as living examples of the kind of leaders we hope our students become.

How Faculty Benefit from Servant Leadership

While much attention is given to how servant leadership benefits students, its impact on faculty is equally as important. Faculty are the heart of teaching and service in higher education, and adopting a servant leadership mindset can enhance the purpose they find in their work.

Stronger relationships and classroom dynamics. Faculty who adopts servant leadership approaches often develop more meaningful relationships with students, which fosters better classroom engagement and cooperation. As Rabin and Smith explain, “teachers who practice an ethic of care consider developing relationships to be artfully complex and at the same time critical to a learning environment” (p. 8, 2013). When professors create spaces where trust and respect are mutual, the classroom becomes a place of safety and comfortability, benefiting both teaching and learning.

Personal fulfillment and emotional connection. The emotional toll of teaching is often overlooked, but faculty who embrace servant leadership experience a deeper sense of purpose in their work. Faculty gain more than content delivery; they gain the joy of watching students thrive and knowing their role made a difference.

Increased satisfaction and retention. Research shows that workplace satisfaction among educators increases when they feel seen and supported. Rabin and Smith talked about that a reflection of heart is critical for both effective teaching and faculty well-being (2013). When institutions encourage servant leadership, they cultivate a better faculty morale, higher job satisfaction, and stronger loyalty to the institution.

Practical Ways Faculty Can Begin Tomorrow

The foundation of servant leadership is intention. Faculty can begin modeling servant leadership in simple ways that foster stronger connections with students, healthier learning environments, and personal fulfillment. Below are three realistic strategies faculty can begin using tomorrow:

1. Practice Intentional Listening

Great teachers listen more than they talk, creating space for students to feel seen and heard. In the classroom, this might look like pausing before responding to student comments, asking follow-up questions that show curiosity, or creating reflection activities that allow the quieter voices to contribute. Being fully present communicates respect and care. Students are more likely to engage, ask questions, and trust the instructor when they feel heard. Faculty often gain feedback into student needs, boosting classroom culture and reducing conflict.

2. Respond with Empathy and Flexibility

Rather than assuming conflict or disrespect, servant leaders seek to understand the reasoning behind student behavior. For example, if a student misses a deadline, a professor might ask about circumstances before being close minded. This also could be seen as staying real with your students, making sure they know you make mistakes as well. Implementing this will make students feel respected and supported. Faculty reduce unnecessary conflict and model emotional intelligence, an essential skill for students to learn.

3. Empower Students with Real Responsibilities

Servant leadership isn’t about doing everything for others, it’s about helping others grow. Faculty can give students meaningful tasks like leading part of a class discussion, mentoring peers, or giving feedback about the course. Offering students that autonomy in the course will result in better trust and relationships. Students will build confidence and ownership over their learning.

Servant leadership in higher education is more than a philosophy, it’s a daily practice that transforms both student outcomes and faculty experiences. When educators lead with care, humility, and a commitment to student growth, they foster an environment where students thrive academically, emotionally, and ethically. By integrating servant leadership principles such as intentional listening, empathetic responses, and student empowerment, professors can cultivate vibrant, respectful, and collaborative learning environments. In doing so, they don’t just teach content, they develop whole people.

Carlee Norris was born and raised in Kansas and has always had a deep love for learning. Her appreciation for strong faculty leadership has shaped her perspective on education and student development. While earning her MBA at Kansas State University, Carlee authored an article exploring how students feel most empowered and how servant leadership can be used to effectively guide and support them. She is passionate about creating spaces where students feel encouraged, understood, and equipped to thrive.

References

LinkedIn. What are the benefits of applying servant leadership in higher education? How Servant Leadership Benefits Higher Education. https://www.linkedin.com/advice/3/what-benefits-applying-servant-leadership-ukuoe#:~:text=Servant%20leadership%20can%20help%20faculty,their%20academic%20freedom%20and%20autonomy

Online, U. of A. (n.d.). Ethics in education: The importance of good leadership. ONLINE. https://online.uark.edu/blog/ethics-in-education-the-importance-of-good-leadership.php

Rabin & Smith. (n.d.). Teaching care ethics: Conceptual understandings and stories for Learning: Journal of Moral Education: Vol 42 , no 2 – get access. https://www.tandfonline.com/doi/full/10.1080/03057240.2013.785942

University, S. C. (n.d.). Care ethics. Markkula Center for Applied Ethics. https://www.scu.edu/ethics/ethics-resources/ethical-decision-making/care-ethics/care-ethics.html

Vicari, C. (2023, July 4). Prioritizing the ethics of care: Why it’s crucial for education in today’s world. LinkedIn. https://www.linkedin.com/pulse/prioritizing-ethics-care-why-its-crucial-education-todays-vicari/

Source link

August 29, 2025
Serving to Lead: The Transformative Role of Servant Leadership in Higher Education – Faculty Focus

Students learn just as much from who we are as educators as they do from what we teach. While content is important, the way faculty members carry themselves, how they communicate, lead, and treat others, can leave an even deeper impact on students’ personal and professional growth. This article explains that educators who use servant leadership principles create trust, empathy, and ethical development in students, ultimately supporting both academic success and long-term character building of students.

Throughout this article, I will explain why servant leadership is especially relevant in higher education today. While using a student-centered philosophy is very beneficial, adopting a servant leadership approach offers an extension of that philosophy. This is not just about teaching content effectively. It is about modeling values like integrity, empathy, and thoughtful decision making in ways students can absorb and carry with them beyond the classroom.
In this paper, I will provide research and real-world examples. I will present practical strategies faculty members where they know their students are walking away a better individual, professionally and personally. I will also discuss how faculty members themselves benefit from adopting a servant leadership model, finding stronger connections with students and a deeper sense of purpose in their work.

Modeling Values in the Classroom

One of the clearest ways servant leadership shows up in higher education is through the modeling of values that students can see and experience in everyday interactions. As Rabin and Smith say, “students learn as much from who we are as what we teach” (p. 2, 2013). This speaks to the purpose of servant leadership in higher education. In the context of the classroom, faculty who embody servant leadership not only communicate course content but also demonstrate values such as compassion and care through their actions.

This leadership approach directly supports student-centered teaching by emphasizing the importance of understanding students’ backgrounds, life circumstances, and unique learning needs. In the research article, Rabin and Smith explain that “to care for students, a teacher needs to know her students well enough to understand their unique motivations and needs” (p. 4). This describes a strong part of servant leadership, placing the needs of others before one’s own and creating relationships built on trust and mutual respect.

When higher education faculty take on the role of servant leaders, they transform learning environments. They challenge the authority-driven classroom stereotype and replace it with a relational one, where students are empowered, respected, and mentored not only academically, but as people. These professors serve as living examples of the kind of leaders we hope our students become.

How Faculty Benefit from Servant Leadership

While much attention is given to how servant leadership benefits students, its impact on faculty is equally as important. Faculty are the heart of teaching and service in higher education, and adopting a servant leadership mindset can enhance the purpose they find in their work.

Stronger relationships and classroom dynamics. Faculty who adopts servant leadership approaches often develop more meaningful relationships with students, which fosters better classroom engagement and cooperation. As Rabin and Smith explain, “teachers who practice an ethic of care consider developing relationships to be artfully complex and at the same time critical to a learning environment” (p. 8, 2013). When professors create spaces where trust and respect are mutual, the classroom becomes a place of safety and comfortability, benefiting both teaching and learning.

Personal fulfillment and emotional connection. The emotional toll of teaching is often overlooked, but faculty who embrace servant leadership experience a deeper sense of purpose in their work. Faculty gain more than content delivery; they gain the joy of watching students thrive and knowing their role made a difference.

Increased satisfaction and retention. Research shows that workplace satisfaction among educators increases when they feel seen and supported. Rabin and Smith talked about that a reflection of heart is critical for both effective teaching and faculty well-being (2013). When institutions encourage servant leadership, they cultivate a better faculty morale, higher job satisfaction, and stronger loyalty to the institution.

Practical Ways Faculty Can Begin Tomorrow

The foundation of servant leadership is intention. Faculty can begin modeling servant leadership in simple ways that foster stronger connections with students, healthier learning environments, and personal fulfillment. Below are three realistic strategies faculty can begin using tomorrow:

1. Practice Intentional Listening

Great teachers listen more than they talk, creating space for students to feel seen and heard. In the classroom, this might look like pausing before responding to student comments, asking follow-up questions that show curiosity, or creating reflection activities that allow the quieter voices to contribute. Being fully present communicates respect and care. Students are more likely to engage, ask questions, and trust the instructor when they feel heard. Faculty often gain feedback into student needs, boosting classroom culture and reducing conflict.

2. Respond with Empathy and Flexibility

Rather than assuming conflict or disrespect, servant leaders seek to understand the reasoning behind student behavior. For example, if a student misses a deadline, a professor might ask about circumstances before being close minded. This also could be seen as staying real with your students, making sure they know you make mistakes as well. Implementing this will make students feel respected and supported. Faculty reduce unnecessary conflict and model emotional intelligence, an essential skill for students to learn.

3. Empower Students with Real Responsibilities

Servant leadership isn’t about doing everything for others, it’s about helping others grow. Faculty can give students meaningful tasks like leading part of a class discussion, mentoring peers, or giving feedback about the course. Offering students that autonomy in the course will result in better trust and relationships. Students will build confidence and ownership over their learning.

Servant leadership in higher education is more than a philosophy, it’s a daily practice that transforms both student outcomes and faculty experiences. When educators lead with care, humility, and a commitment to student growth, they foster an environment where students thrive academically, emotionally, and ethically. By integrating servant leadership principles such as intentional listening, empathetic responses, and student empowerment, professors can cultivate vibrant, respectful, and collaborative learning environments. In doing so, they don’t just teach content, they develop whole people.

Carlee Norris was born and raised in Kansas and has always had a deep love for learning. Her appreciation for strong faculty leadership has shaped her perspective on education and student development. While earning her MBA at Kansas State University, Carlee authored an article exploring how students feel most empowered and how servant leadership can be used to effectively guide and support them. She is passionate about creating spaces where students feel encouraged, understood, and equipped to thrive.

References

LinkedIn. What are the benefits of applying servant leadership in higher education? How Servant Leadership Benefits Higher Education. https://www.linkedin.com/advice/3/what-benefits-applying-servant-leadership-ukuoe#:~:text=Servant%20leadership%20can%20help%20faculty,their%20academic%20freedom%20and%20autonomy

Online, U. of A. (n.d.). Ethics in education: The importance of good leadership. ONLINE. https://online.uark.edu/blog/ethics-in-education-the-importance-of-good-leadership.php

Rabin & Smith. (n.d.). Teaching care ethics: Conceptual understandings and stories for Learning: Journal of Moral Education: Vol 42 , no 2 – get access. https://www.tandfonline.com/doi/full/10.1080/03057240.2013.785942

University, S. C. (n.d.). Care ethics. Markkula Center for Applied Ethics. https://www.scu.edu/ethics/ethics-resources/ethical-decision-making/care-ethics/care-ethics.html

Vicari, C. (2023, July 4). Prioritizing the ethics of care: Why it’s crucial for education in today’s world. LinkedIn. https://www.linkedin.com/pulse/prioritizing-ethics-care-why-its-crucial-education-todays-vicari/

Source link

August 29, 2025
$Math is Out, Cake is In: Introducing Students to Rubrics – Faculty Focus$

Math is Out, Cake is In: Introducing Students to Rubrics – Faculty Focus
Rubrics are a valuable tool that supports student growth and facilitates instructor grading and feedback (Suskie, 2018). As instructors, we see this value; unfortunately, many of our students, especially first-year students, are unfamiliar with the concept. This presents an opportunity to raise their awareness of a tool that will benefit them as they master concepts and seek course success.

The Context

During a two-week mathematics-focused summer bridge program, I teach a segment called Behind the Scenes, which highlights various success strategies students will need to leverage to be successful in college. One lesson exposes these students to the concept of rubrics, covering everything from their purpose, structure, and application.

The Illustration

As noted by Doyle and Zakrajsek (2018), “The human brain is constantly looking for connections. Connections help you use prior knowledge to build bridges to the new material, creating a more meaningful understanding of the new material” (p. 15). Building off this idea, the lesson begins with an illustration about desserts, specifically cake, a concept which will resonate with most if not all students. The following instructions are given:

I took a vote and decided that math is out, cake is in. Cake has much more bearing on your future than math. Your assignment is to design the cake I want to eat for lunch today. You will be graded on this, and it will be worth a lot of points. Take a couple of minutes to sketch out your design, making note of the details. You will not be graded on your artistic ability or lack of materials.

As students are working, I’m walking around the room. Sometimes, they will ask me questions about my preferences. Much to their dismay, I tell them I am not answering questions at that time, but that they will understand why in a few minutes.

The Discussion

I ask for volunteers to share their designs with me. As the student shares, I find a reason why I do not want to eat their cake. Perhaps there are not enough layers, or it’s not my favorite flavor. I will then subjectively give them a grade, loosely based on the rubric I will eventually show them, but with an element of randomness to aid the discussion. Finally, I’ll show them a picture of the right answer: a three-layer lemon cake decorated with pale yellow frosting, lemon slices, and a few small flowers.

I then pose a series of questions for discussion:
- What did you think of my grading?
- Was it subjective or objective? Why?
- If we repeated this assignment, what would you want to know to meet my expectations?
Understandably, students are not a fan of how I graded, correctly identifying the subjective nature. Based on the grading and feedback they heard me give the volunteers, they can identify several criteria that would be helpful to know in advance. As we talk through these ideas, I make the connection that in higher education the tool we use to be transparent about expectations and objectively grade assessments is called a rubric. Of course, I also backtrack and tell them, “FYI, math is back in – turns out it is useful.”

Once we have defined a rubric, we talk about the structure. I present to them a sample cake rubric, and talk about the grid format, with rows representing the criteria we will be evaluating (layers, flavor, decoration); columns representing the performance rating (needs improvement, proficient, advanced); and the cells containing descriptions of performance and point values (0, 5, 10).

The Application

After we establish what a rubric is and how instructors use it, we turn towards application of rubrics as a way for them to pre-grade their assignments before submitting. Each student in the program will be enrolled in a first-year seminar their first term, which becomes the example from which they can practice. I pass out the first-year seminar rubric along with the assignment prompts, which are consistent throughout the course. I also provide two sample responses, one that clearly does not meet expectations (43 words total), and one that does meet expectations (270 words total). I ask them to work in pairs to review the submissions and grade them using the given rubric, which includes four criteria and two performance levels.

Pairs are then asked to report out their score for the first submission, and we talk through their decisions. I share with them my grade as well. This process is repeated for the second submission. We also discuss what they liked about the experience and what they found challenging. In all, this lesson takes about thirty minutes but could be shortened or lengthened as needed.

Doyle and Zakrajsek (2018) further identified that “when learning something new, it helps to be interested in it, see a value to it, pay attention to it, associate it with something you already know, and practice it a lot” (p. 100). The cake illustration is something they already know, and let’s be honest, if it relates to food, college students are very interested. Through the absurd example, they see the value which piques their curiosity and attention. The hands-on practice, while maybe not sufficient in and of itself, at least provides some experience with a new idea from which they can build their confidence.

This rubric lesson not only raises awareness of a grading technique they will encounter over their time in college, but they get practice with an actual rubric that will be used in their first term. Further, their review of poor and exemplary submissions can help frame the direction they need to take when it comes to their own assignments. As McGuire (2015) pointed out, one way “to help your students gain competence is giving them targeted feedback, rubrics, and exemplars” (p. 88).

When students understand the purpose of the rubric, they are better positioned to achieve success in their courses. Proactively, it allows them to assess whether they are meeting assignment expectations before the due date. Reactively, it allows them to understand why they missed points, leveraging the feedback as formative assessment and making corrections for future assignments.

Sarah A. Forbes, PhD, is the Student Academic Success Director and a first-year seminar instructor at Rose-Hulman Institute of Technology. In these roles, she helps students learn new strategies for academic success. Sarah also serves as a first-year seminar instructional designer, summer bridge program director, and academic advising program administrator.

References

Doyle, T., & Zakrajsek, T. D. (2018). The new science of learning: How to learn in harmony with your brain. Stylus Publishing.

McGuire, S. Y. (2015). Teach students how to learn. Stylus Publishing.

Suskie, L. (2018). Assessing student learning: A common sense guide. John Wiley & Sons, Incorporated.
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August 27, 2025

Tag: Focus

Empathize with Shy Students in the Classroom

Class Participation

Power Dynamics in the Classroom

Not Knowing the Unwritten Rules of College

Being Different, Blazing New Trails

Early Advice that All College Students Need to Hear

Protect Your Grade Point Average (GPA)

Get Noticed in the Classroom. Instructors Write References.

Refine Essential Skills

Find a Mentor to Guide You

Active Outreach Facilitates Student Development and Supports Retention

Step 1: Students describe how they used GenAI, according to our AI scale

Step 2: Students defend their use or non-use of GenAI

Final Thoughts

Why Students Turn to AI

Process Over Product

Disciplinary Innovation

Creative Disciplines

Simulating Complexity

Modeling Scientific Reasoning

Building AI Fluency Across the Curriculum