Tag: Teaching

Beyond Evaluation: Using Peer Observation to Strengthen Teaching Practices – Faculty Focus
University faculty members are usually immersed in various tasks related to teaching, research, committees, and office hours. As a result, they rarely have time to explore the practices of their colleagues or seek advice on their own, which leads them to work in isolation. This isolation can hinder their professional development and growth, especially if they do not take the time to reflect on their own teaching methods and practices. Ultimately, student learning is impacted. One effective practice that can break this cycle is peer observation.

Peer observation is a valuable university practice with mutual benefits for the observer and observee, with an emphasis on learning, reflection, and improvement to enhance teaching methods and overall practices. Exploring peer observation models, their stages, and practical methods and resources for use in higher education are all part of this process. By fostering a culture of continuous improvement through pedagogical observation, educators engage in dialogue to share insights, plan for instructional improvement, self-reflect, adopt effective strategies, and refine teaching practices.

Models of Peer Observation

The focus of the peer observation defines its goal, context, and the way the participants act and feel toward the process. Based on the focus, peer observations can be classified into two models: formative-developmental and summative-evaluative.

While the formative-developmental observation aims to enhance and promote the improvement of teaching skills within a supportive and collegial context, the summative-evaluative observation aims to evaluate and judge the faculty based on the observation. Therefore, if an institution aims at improving the teaching practices of its faculty members and wants them to engage in professional development activities that they welcome and accept, it should incorporate a formative-developmental type of observation.

Although summative-evaluative peer observations are needed to evaluate the teaching skills of faculty members, we believe that it is a fair practice for the faculty to experience formative-developmental observations several times and to receive the necessary support before they undergo an evaluative observation especially if it is high-stakes in that it impacts their job retention or promotion. In our discussion, we will focus on formative-developmental observations because we are interested in practices that help in the improvement of teaching practices in educational institutions.

Benefits of Formative-developmental Observations

When approached as a developmental or formative practice, peer observations afford faculty valuable opportunities to improve their practices and grow professionally. This is mainly true if observations are done periodically, rather than as one-shot classroom visits, when the outcomes of one observation feed into another, and when on-going dialogue between the observer and the observee takes place. The practices of the process of classroom observations are critical as they:
- Encourage professional growth: Formative observations provide faculty with structured opportunities to enhance their teaching skills and improve student learning outcomes.
- Facilitate reflective practice: Formative observations enable faculty to critically assess their teaching methods and refine their instructional approaches.
- Support learning in and from practice: As highlighted by Ball and Cohen (1999), learning is most effective when embedded in practice, making peer observations a powerful tool for professional development.
- Promote the haring of best practices: Faculty can observe, discuss, and integrate effective teaching strategies used by their peers.
- Challenge teaching assumptions: Through peer feedback and reflection, faculty can identify and modify ingrained teaching habits to enhance student engagement.
- Build confidence in implementing new strategies: Observing peers in action helps faculty see new techniques in practice, making them more confident in adopting these approaches.
- Enhance course content and delivery: observations allow for a structured review of both the material taught and the teaching methods used, leading to refinements in instruction.
- Strengthen collegial support and collaboration: Collegial observations enhance a culture of open dialogue among faculty, fostering a supportive and collaborative academic environment.
- Improve teaching and learning quality: Observations encourage continuous improvement in instructional effectiveness, ultimately benefiting student learning experiences.
Stages of Formative-developmental Observations

Peer observations have numerous merits. They can be perceived as a cyclic process, each cycle including a peer observation session that is preceded and followed by meetings between the observer and the faculty whose class is to be observed, thus making it a three-stage process. Each of the stages has its benefits and contributes to the overall outcome of the process if done with a clear purpose in a safe, collegial, and supportive context. The three stages are:
- Pre-observation: To introduce the process and set the stage for effective peer observation, it is necessary to discuss the observation goals and establish mutual expectations for the process, in addition to discussing the session learning outcomes, learning activities and any content or documents that will be used during the observed session. This stage helps the observer clarify the goal of the observation and establishes an atmosphere of collaboration between the observer and the observee which reduces anxiety on the part of the observee. Moreover, it allows the observee to clarify the lesson context and allows for customization of the lesson before the observation takes place.
- Lesson Observation: This includes observing a teaching session with all its happenings such as teaching strategies, students’ engagement, modes of interaction, indicators of learning, assessment tasks, questioning techniques, and learning activities. At this stage, the observer collects notes describing what is observed without making any judgment about the performance of the teacher. To this end, we prefer using the narrative log as it enables the observer to document the timing of behaviors, techniques, and actions of both the observed instructor and the students. These are recorded objectively; i.e., recording solely what is observed without including any of the observer’s judgments. However, the observer can add comments or questions about the classroom events to be discussed later in the post-observation meeting. Narrative logs are considered an effective tool to describe verbal and non-verbal behavior, highlighting what the observer sees rather than making judgements (Chism, 2007). To this end, the observer must keep in mind the golden rule: Learning to see, unlearning to judge.
- Post- observation: At this stage, discussion of the class activities and reflection occurs. The observer uses the observation notes collected during the lesson observation to lead the discussion and to refer to for evidence for the different points being discussed. The observee reflects on the class; and the observer, to provide helpful feedback, seeks to understand more about the classroom by asking questions for clarification or to gain a clearer understanding of the lesson, students, and content. An excellent tool designed by Project-Zero at Harvard University is the Ladder of Feedback, which can help structure the discussion to be constructive, supportive and growth-promoting in a non-threatening manner.
The post-observation meeting should occur as soon as possible after the observation. The discussion in this stage should lead to an action plan. Therefore, as a good practice, the observer can summarize the points discussed in this meeting, emphasize points of strength, provide suggestions toward improving teaching practices, and agree with the observee on an action plan for improvement. The date for the following observation or meeting can be scheduled at the end of this stage.

How to Encourage Peer Observation in Higher Education

Although peer observation is a vital practice for professional development, it is not well-perceived by faculty members, and it is not a common practice in universities. Faculty members are mostly reluctant to voluntarily participate in peer observation. Thus, we recommend that universities start developing a culture of trust and collegiality to support this practice. At the personal level of faculty members, they can be encouraged to ask their colleagues to observe their classrooms and collect notes during the observations. To sharpen this practice and make it more relevant to one’s instructional needs, the observee can set specific goals for the observation. These suggestions can help promote the culture where peer observation becomes a practice for continuous, accessible professional development that can transform universities into learning communities and make them better learning spaces for their faculty members.

Dr. Siham Al Amoush is an experienced educator specializing in teacher education and higher education professional development, she obtained her PhD in Chemistry Education from Bremen University, where she researched and compared teacher beliefs in Jordan, Turkey, and Germany. She has published several papers in international journals on topics such as teacher beliefs, curriculum design, and technology integration in the classroom. Passionate about improving teacher education and supporting educators to reach their full potential, she aims to make a difference in the education system through collaboration between different sectors. Currently, she works in faculty development at the Excellence in Teaching and Learning Center at Qatar University.

Dr. Amal Farhat is an experienced educator and instructional coach with a background in both K-12 and higher education across the private and public sectors. She is interested in fostering professional learning communities through positive, constructive collaboration among all institutional stakeholders. In higher education, she has taught future educators and supported faculty in enhancing their teaching practices. Dr. Farhat holds a PhD in Education from the Lebanese University, a Counseling in Pedagogy diploma from St. Joseph University, and a CAEL certification from Harvard Graduate School of Education.
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April 2, 2025
Beyond Evaluation: Using Peer Observation to Strengthen Teaching Practices – Faculty Focus
University faculty members are usually immersed in various tasks related to teaching, research, committees, and office hours. As a result, they rarely have time to explore the practices of their colleagues or seek advice on their own, which leads them to work in isolation. This isolation can hinder their professional development and growth, especially if they do not take the time to reflect on their own teaching methods and practices. Ultimately, student learning is impacted. One effective practice that can break this cycle is peer observation.

Peer observation is a valuable university practice with mutual benefits for the observer and observee, with an emphasis on learning, reflection, and improvement to enhance teaching methods and overall practices. Exploring peer observation models, their stages, and practical methods and resources for use in higher education are all part of this process. By fostering a culture of continuous improvement through pedagogical observation, educators engage in dialogue to share insights, plan for instructional improvement, self-reflect, adopt effective strategies, and refine teaching practices.

Models of Peer Observation

The focus of the peer observation defines its goal, context, and the way the participants act and feel toward the process. Based on the focus, peer observations can be classified into two models: formative-developmental and summative-evaluative.

While the formative-developmental observation aims to enhance and promote the improvement of teaching skills within a supportive and collegial context, the summative-evaluative observation aims to evaluate and judge the faculty based on the observation. Therefore, if an institution aims at improving the teaching practices of its faculty members and wants them to engage in professional development activities that they welcome and accept, it should incorporate a formative-developmental type of observation.

Although summative-evaluative peer observations are needed to evaluate the teaching skills of faculty members, we believe that it is a fair practice for the faculty to experience formative-developmental observations several times and to receive the necessary support before they undergo an evaluative observation especially if it is high-stakes in that it impacts their job retention or promotion. In our discussion, we will focus on formative-developmental observations because we are interested in practices that help in the improvement of teaching practices in educational institutions.

Benefits of Formative-developmental Observations

When approached as a developmental or formative practice, peer observations afford faculty valuable opportunities to improve their practices and grow professionally. This is mainly true if observations are done periodically, rather than as one-shot classroom visits, when the outcomes of one observation feed into another, and when on-going dialogue between the observer and the observee takes place. The practices of the process of classroom observations are critical as they:
- Encourage professional growth: Formative observations provide faculty with structured opportunities to enhance their teaching skills and improve student learning outcomes.
- Facilitate reflective practice: Formative observations enable faculty to critically assess their teaching methods and refine their instructional approaches.
- Support learning in and from practice: As highlighted by Ball and Cohen (1999), learning is most effective when embedded in practice, making peer observations a powerful tool for professional development.
- Promote the haring of best practices: Faculty can observe, discuss, and integrate effective teaching strategies used by their peers.
- Challenge teaching assumptions: Through peer feedback and reflection, faculty can identify and modify ingrained teaching habits to enhance student engagement.
- Build confidence in implementing new strategies: Observing peers in action helps faculty see new techniques in practice, making them more confident in adopting these approaches.
- Enhance course content and delivery: observations allow for a structured review of both the material taught and the teaching methods used, leading to refinements in instruction.
- Strengthen collegial support and collaboration: Collegial observations enhance a culture of open dialogue among faculty, fostering a supportive and collaborative academic environment.
- Improve teaching and learning quality: Observations encourage continuous improvement in instructional effectiveness, ultimately benefiting student learning experiences.
Stages of Formative-developmental Observations

Peer observations have numerous merits. They can be perceived as a cyclic process, each cycle including a peer observation session that is preceded and followed by meetings between the observer and the faculty whose class is to be observed, thus making it a three-stage process. Each of the stages has its benefits and contributes to the overall outcome of the process if done with a clear purpose in a safe, collegial, and supportive context. The three stages are:
- Pre-observation: To introduce the process and set the stage for effective peer observation, it is necessary to discuss the observation goals and establish mutual expectations for the process, in addition to discussing the session learning outcomes, learning activities and any content or documents that will be used during the observed session. This stage helps the observer clarify the goal of the observation and establishes an atmosphere of collaboration between the observer and the observee which reduces anxiety on the part of the observee. Moreover, it allows the observee to clarify the lesson context and allows for customization of the lesson before the observation takes place.
- Lesson Observation: This includes observing a teaching session with all its happenings such as teaching strategies, students’ engagement, modes of interaction, indicators of learning, assessment tasks, questioning techniques, and learning activities. At this stage, the observer collects notes describing what is observed without making any judgment about the performance of the teacher. To this end, we prefer using the narrative log as it enables the observer to document the timing of behaviors, techniques, and actions of both the observed instructor and the students. These are recorded objectively; i.e., recording solely what is observed without including any of the observer’s judgments. However, the observer can add comments or questions about the classroom events to be discussed later in the post-observation meeting. Narrative logs are considered an effective tool to describe verbal and non-verbal behavior, highlighting what the observer sees rather than making judgements (Chism, 2007). To this end, the observer must keep in mind the golden rule: Learning to see, unlearning to judge.
- Post- observation: At this stage, discussion of the class activities and reflection occurs. The observer uses the observation notes collected during the lesson observation to lead the discussion and to refer to for evidence for the different points being discussed. The observee reflects on the class; and the observer, to provide helpful feedback, seeks to understand more about the classroom by asking questions for clarification or to gain a clearer understanding of the lesson, students, and content. An excellent tool designed by Project-Zero at Harvard University is the Ladder of Feedback, which can help structure the discussion to be constructive, supportive and growth-promoting in a non-threatening manner.
The post-observation meeting should occur as soon as possible after the observation. The discussion in this stage should lead to an action plan. Therefore, as a good practice, the observer can summarize the points discussed in this meeting, emphasize points of strength, provide suggestions toward improving teaching practices, and agree with the observee on an action plan for improvement. The date for the following observation or meeting can be scheduled at the end of this stage.

How to Encourage Peer Observation in Higher Education

Although peer observation is a vital practice for professional development, it is not well-perceived by faculty members, and it is not a common practice in universities. Faculty members are mostly reluctant to voluntarily participate in peer observation. Thus, we recommend that universities start developing a culture of trust and collegiality to support this practice. At the personal level of faculty members, they can be encouraged to ask their colleagues to observe their classrooms and collect notes during the observations. To sharpen this practice and make it more relevant to one’s instructional needs, the observee can set specific goals for the observation. These suggestions can help promote the culture where peer observation becomes a practice for continuous, accessible professional development that can transform universities into learning communities and make them better learning spaces for their faculty members.

Dr. Siham Al Amoush is an experienced educator specializing in teacher education and higher education professional development, she obtained her PhD in Chemistry Education from Bremen University, where she researched and compared teacher beliefs in Jordan, Turkey, and Germany. She has published several papers in international journals on topics such as teacher beliefs, curriculum design, and technology integration in the classroom. Passionate about improving teacher education and supporting educators to reach their full potential, she aims to make a difference in the education system through collaboration between different sectors. Currently, she works in faculty development at the Excellence in Teaching and Learning Center at Qatar University.

Dr. Amal Farhat is an experienced educator and instructional coach with a background in both K-12 and higher education across the private and public sectors. She is interested in fostering professional learning communities through positive, constructive collaboration among all institutional stakeholders. In higher education, she has taught future educators and supported faculty in enhancing their teaching practices. Dr. Farhat holds a PhD in Education from the Lebanese University, a Counseling in Pedagogy diploma from St. Joseph University, and a CAEL certification from Harvard Graduate School of Education.
Source link
April 2, 2025
AI-Powered Teaching: Practical Tools for Community College Faculty – Faculty Focus
Artificial intelligence (AI) has transitioned from a speculative concept to a transformative tool in higher education, particularly within community colleges. This article explores AI’s historical evolution, critically assesses its benefits and challenges for teaching, and provides evidence-based strategies for faculty to integrate AI effectively. Drawing on the scholarship of teaching and learning (SoTL), it argues that AI can enhance accessibility and efficiency while preserving the human essence of education. A case study from a California community college illustrates these principles in action. Faculty developers and instructors can use this framework to harness AI’s potential, ensuring it supports rather than supplants their pedagogical roles.

Introduction

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

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

Historical Context: AI’s Evolution and Educational Implications

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

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

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

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

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

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

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

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

Practical Applications: Strategies for Faculty Development

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

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

Conclusion

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Introduction

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

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

Historical Context: AI’s Evolution and Educational Implications

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

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

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

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

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

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

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

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

Practical Applications: Strategies for Faculty Development

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

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

Conclusion

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
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March 31, 2025
Bowdoin to Devote $50M Gift to AI Learning, Teaching

Bowdoin College has received a $50 million gift from Reed Hastings, 1983 alumnus, Netflix cofounder and Powder Mountain CEO, to create the Hastings Initiative for AI and Humanity.

The gift, the largest in the college’s 231-year history, will be used largely to support teaching and research related to artificial intelligence. It will pay for 10 new faculty members, expand faculty-led research and curriculum offerings, and drive campuswide conversations about the benefits and challenges of AI.

“This donation seeks to advance Bowdoin’s mission of cultivating wisdom for the common good by deepening the College’s engagement with one of humanity’s most transformative developments: artificial intelligence,” Hastings said in a press release. “As AI becomes smarter than humans, we are going to need some deep thinking to keep us flourishing.”

Hastings credited a late Bowdoin mathematics professor, Steve Fisk, for first encouraging him to study AI. “Steve was about forty years too early, but his perspective was life-changing for me,” Hastings said.

“We are thrilled and so grateful to receive this remarkable support from Reed, who shares our conviction that the AI revolution makes the liberal arts and a Bowdoin education more essential to society,” Bowdoin president Safa Zaki said in a statement.

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March 25, 2025
Product Enhancements from Discovery Education Foster Improved Engagement and Personalization
Charlotte, NC — Discovery Education, the creators of essential K-12 learning solutions used in classrooms around the world, today announced a host of exciting product updates during a special virtual event led by the company’s Chief Product Officer Pete Weir. Based on feedback from the company’s school-based partners, these updates make teaching and learning even more relevant, engaging, and personalized for users of Discovery Education products.

Among the enhancements made to Discovery Education Experience, the essential companion for engaged K-12 classrooms that inspires teachers and motivates students, are: teachers and motivates students, are:
- Improved Personalized Recommendations for Teachers: With thousands of resources in Experience, there is something for every classroom. The new Core Curriculum Complements feature in Experience automatically surfaces engaging resources handpicked to enhance school systems’ core curriculum, simplifying lesson planning and ensuring tight alignment with district priorities. Additionally, Experience now offers educators Personalized Content Recommendations. These content suggestions made to individual teachers are based on their unique profiles and preferences, or what is frequently used by other educators like them.
- An Enhanced AI-Powered Assessment Tool: Originally launched in 2024, this tool is the first in a new suite of AI-powered teaching tools currently under development, and it empowers educators to create high-quality assessments using vetted resources right from within Experience. Educators can now more easily customize assessments according to reading level, question type, Bloom’s Taxonomy, and more – ensuring optimal learning experiences for students. Educators can also review and tailor the questions and, once ready, export those questions into a variety of formats.
- A New Career Exploration Tool for All Discovery Education Experience Users: Career Connect – the award-winning tool that connects K-12 classrooms with real industry professionals – is now accessible to all Discovery Education Experience users. With this new feature, classrooms using Experience can directly connect to the professionals, innovations, and skills of today’s workforce. Furthermore, Experience is now delivering a variety of new career pathway resources, virtual field trips, and career profiles – building career awareness, inviting exploration, and helping students prepare for their future.
- A newly enhanced Instructional Strategy Library: To elevate instruction and better support teachers, Discovery Education has enhanced its one-stop-spot for strategies supporting more engaging, efficient, and effective teaching. The improved Instructional Strategy Library streamlines the way educators find and use popular, research-backed instructional strategies and professional learning supports and provides connected model lessons and activities.
Also announced today were a host of improvements to DreamBox Math by Discovery Education. DreamBox Math offers adaptive, engaging, and scaffolded lessons that adjust in real time to personalize learning so that students can build confidence and skills at their own pace. Among the new improvements to DreamBox Math are:
- Major Lesson Updates: Based on teacher feedback, Discovery Education’s expert curriculum team has updated DreamBox Math’s most popular lessons to make them easier for students to start, play, and complete successfully. Students will now encounter lessons with updated scaffolding, enhanced visuals, greater interactivity, and added context to ground mathematical concepts in the curriculum and the world they live in.
- A New Look for Middle School: Middle school students will encounter a more vibrantly colored and upgraded user interface featuring a reorganized Lesson Chooser whose intuitive design makes it easy to identify teacher-assigned lessons from their personalized lesson options. Additional updates will follow throughout the year.
- New Interactive Curriculum Guide: Discovery Education has strengthened the link between DreamBox Math and school systems’ core instruction with an Interactive Curriculum Guide. Educators can now explore the breadth and scope of DreamBox content by grade and standard to locate, preview, and play lessons, increasing familiarity with lessons, and enhancing targeted instruction. The DreamBox Math team will continue to make updates to standards and curriculum alignments throughout the year.
To watch a replay of today’s special event in its entirety, and to learn about additional updates to Discovery Education’s suite of K-12 solutions, visit this link.

“Discovery Education understands teachers’ sense of urgency about closing the achievement gaps highlighted by recent NAEP scores,” said Pete Weir, Discovery Education’s Chief Product Officer. “In response, we accelerated the development and deployment of what has traditionally been our ‘Back-to-School’ product enhancements. The stakes for our students have never been higher, and Discovery Education is dedicated to putting the highest-quality, most effective resources into teachers and students’ hands as soon as possible.”

For more information about Discovery Education’s award-winning digital resources and professional learning solutions, visit www.discoveryeducation.com, and stay connected with Discovery Education on social media through X, LinkedIn, Instagram, TikTok, and Facebook.

eSchool Media staff cover education technology in all its aspects–from legislation and litigation, to best practices, to lessons learned and new products. First published in March of 1998 as a monthly print and digital newspaper, eSchool Media provides the news and information necessary to help K-20 decision-makers successfully use technology and innovation to transform schools and colleges and achieve their educational goals.

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March 21, 2025
A new era for teachers as AI disrupts instruction

This story originally appeared on the Christensen Institute’s blog, and is reposted with permission.

Key points:

Picture your favorite teacher from your childhood. He or she may have been great at explaining things, energetic, affirming, funny, or had other wonderful attributes. I remember Mrs. Rider. She was smart and pretty, and showed she really believed in me.

With this picture in mind that highlights the many wonderful teachers who typify the “sage on the stage” teacher role, you may wonder why Guide School (full disclosure: I’m the founder) prepares teachers and other adults to become “guides” instead of sages. Why not spend our efforts developing more wonderful sages like Mrs. Rider?

The printing press provides a helpful analogy to answer that question.

Over time, Disruptive Innovations change how things are conventionally done

Before the invention of the printing press, books and written materials were primarily produced as handwritten manuscripts. Scribes, often monks or other church officials, painstakingly copied texts by hand using quill pens and special inks to illuminate and decorate each parchment.

The printing press, invented by Johannes Gutenberg in the 15th century, revolutionized the production and sharing of written knowledge. It allowed for the mass production of books at a much faster rate and lower cost. In short, texts became accessible to a greater number of people.

But it also meant disrupting the profession of scribes, who suddenly found their work had shifted. Some scribes found new opportunities as proofreaders or editors within the emerging print industry. Others continued to provide handwritten services for personal letters and legal documents. Additionally, a market remained for beautifully handcrafted manuscripts among wealthy patrons who valued calligraphy.

There’s a parallel between the stories of scribes and conventional teachers. Just as the best scribes produced unique artistry in rare, individually commissioned works, the best teachers create rare but enviable classrooms with well-behaved, deeply motivated, impressively thriving students. Unfortunately, however, many people are left out of these ideal scenarios. Without the printing press, millions of people would have languished without access to printed materials. Without transforming the conventional classroom, millions of students today will continue to suffer from want of effective instruction. That’s because while the conventional system could develop more wonderful, conventional teachers like Mrs. Rider, doing so requires an investment of resources often unavailable to every student in every school across the world. All too often, only those who are lucky or whose families can pay receive the benefits of those investments.

Happily, the printing press’s disruption of scribing proved to be an irrefutable boon for the education of humanity. The printing press facilitated the growth of literacy, numeracy, and scientific knowledge by enabling the widespread distribution of printed materials with dependable accuracy and lower costs. It played a crucial role in the Renaissance, Reformation, and Scientific Revolution, allowing for the mass sharing of ideas at unprecedented speed and scale. By the end of the 15th century, millions of copies of thousands of book titles had been printed, marking a dramatic shift in the accessibility of knowledge.

AI and its potential to disrupt conventional teaching

Similarly, the rise of AI-powered, online apps for instruction is disrupting the teaching profession. It’s giving rise to a new wave of global knowledge distribution with increasingly dependable accuracy and precision, allowing for mass learning at unprecedented speed and scale.

When the printing press arrived, the scribe profession did not disappear, but scribes did have to adapt to new roles as their industry changed. Similarly, many conventional teachers will need to adapt to a new role as their role of sage becomes disrupted.

Fortunately, this pivot presents a remarkable opportunity for teachers and society at large. For years, experts have identified that students do best when they have personal, individual tutelage to help them learn. Top-down, whole-class, monolithic instruction isn’t working for most students–and observant teachers know that. The shift from sage on the stage to guide on the side of each student is a welcome relief for teachers who see that the conventional approach is broken in that it leaves behind too many students and want a model that allows them to have the individual impact they hoped for when they entered the teaching profession.

AI frees up teachers’ time to give more individual attention and students’ time for more than foundational knowledge attainment. The Flex blended-learning model, which pairs AI-powered apps with group discussions, real-world projects, individual coaching from guides, and other student experiences, attracts teachers who see its value and want its benefits. Rather than feeling replaced by computer-based instruction, these teachers feel attracted to a clear opportunity to shift their time spent on lectures and embrace the facilitation of a more student-driven learning design for their students.

Guide School prepares adults who feel called to this new role. The guide profession is different from the conventional teaching profession. It requires different mindsets, skills, and dispositions. But for those well-suited to and trained for the role, it’s a profession with unprecedented opportunities to help youth worldwide develop knowledge and talents to a higher level than ever before.

Heather Staker, The Christensen Institute

Heather Clayton Staker is an adjunct fellow at the Christensen Institute, specializing in K–12 blended learning. She is the co-author of “Blended” and “The Blended Workbook.” She is the founder and CEO of Guide School (www.guide.school), which helps teachers implement a software-led “Flex” instructional model and elevate their role to world-class mentor and guide.

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March 14, 2025
Peter Elbow was right about teaching writing

In the New York Times obituary of Peter Elbow, the giant of composition studies, he is said to have “transformed freshman comp,” which he definitely did, but also, maybe not?

Even as someone who has done his fair share of thinking and writing about teaching writing, I did not realize that his landmark book, Writing Without Teachers, was first published all the way back in 1973. For sure, the approach to writing he advocated for in Writing Without Teachers and subsequent books challenged the prevailing dogma of academic writing by emphasizing freedom, student agency and audience above correctness and authority, but to consider the full import of Elbow’s message and compare it to what happens in writing classrooms, it’s tough to see a full “transformation” at work.

At the time I started teaching freshman composition as a graduate TA (1994), I had never heard of Peter Elbow, and none of the people tasked with preparing me for the job introduced me to his work. In fact, I would not encounter Elbow until 2001, when I expressed frustration with teaching through the lens of rhetorical “modes” and how I wished that I could get students writing more freely and authentically because I was tired of reading performative B.S. written for a grade.

“You should try Peter Elbow,” I was told. I did, and it was like the clouds suddenly parted and I could see the sun for the first time. Anyone who teaches writing as a process, who uses peer review and reflection, is working from Elbow-ian DNA. This surely fits any definition of transformation, doesn’t it?

But also, why was I not introduced to Peter Elbow as a beginning writing teacher? Why, at the time I did discover him, were departments still teaching rhetorical modes, or composition as (essentially) essays responding to literature?

In hindsight, I can tell that Elbow’s views on writing must have had a significant impact on the kind of writing I was asked to do in school and how I did it. I’ve written extensively how my grade school teachers of the 1970s privileged creativity and writing problem solving over correctness, engendering a lifelong curiosity about how writing works.

But by the time I was a teacher, it seems as though whatever transformation Elbow had caused had been beaten back, at least to some degree. Focus on process and revision remained, but this process was deployed in the making of very standard, significantly prescriptive artifacts that were easy to explain, straightforward to grade—as they fit established rubrics—and (at least in my experience) largely uninteresting to read and (in the experience of many students) uninteresting to write.

It isn’t surprising that attempts at giving students room to maneuver, which make it difficult to compare them to each other or standards of sufficiency, are resisted by those who prefer order to exploration. The most popular composition textbook of recent years is They Say/I Say (well over a million copies sold) a book that literally coaches students to write using Mad Libs–style templates to imitate forms of academic writing, under the theory students will learn academic expression through osmosis.

Having tried this book for half a semester, I understand its appeal. It’s really just a more refined version of the prescriptive process I used in the 1990s teaching rhetorical modes. If your primary goal is to have students turn in an artifact that resembles the kind of writing that would be produced through a scholarly process, it is very handy.

If the goal is to get students to think like scholars or go through a process that requires them to wrestle with the genuine challenges of academic inquiry and expression, it is a lousy choice. These are simulations of academic artifacts, predating the simulations now easily created by large language models like ChatGPT.

The orderly logic of “schooling” seems to repeatedly win over the mess and chaos of learning. Elbow argued that discovery and differentiation was the highest calling of the learning process, and that writing was an excellent vehicle for fulfilling this calling. This requires one to get comfortable with discomfort. For some reason this is serially viewed as a kind of threat to school, rather than what it should be, the focus of the whole enterprise.

The New York Times obituary calls Elbow’s approach a “more reflective and touchy-feely process,” which I read a signal as to the lack of rigor of the approach, but in truth, it’s the opposite. There’s nothing particularly rigorous about compliance, particularly when enforced by an authority above with all the power, like a teacher wielding their grade book.

As I’ve found over and over in my career, including weekly in this space for the last 13 years, there is nothing more demanding than being asked to deliver a thought that could only come from your unique intelligence. There is also nothing more interesting for both the writer and the reader.

Ultimately, I evolved in ways that make me not quite a full Elbow-ian. The experiences in The Writer’s Practice are structured in ways that do not quite square entirely with Writing With Teachers, though even as I write this sentence, I cannot help but note that calling the assignments in the book experiences, and the fact that I wrote the book in such a way that it could be engaged in the absence of a teacher, suggests that maybe the gap isn’t as wide as I perceive.

While I was working on the manuscript of what would come to be called More Than Words: How to Think About Writing in the Age of AI, I would play around with possible titles, as the title on the proposal—“Writing With Robots”—was used for the purpose of getting attention for a book proposal, not something that genuinely reflected the sentiments of the book I planned to write.

One of the titles I considered was “Everyone Should Write,” a reference to one of Elbow’s later collected volumes, Everyone Can Write.

One of the gifts of the existence of large language models has been to demonstrate the gap between machine prose and that which can be produced by a unique human intelligence. In a way, this only revalidates Elbow’s original insights of Writing Without Teachers, that we, as humans, have a higher purpose than producing school artifacts for a grade.

I’m not giving up hope that we can accept this gift.

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March 6, 2025
The case against impartial university teaching
“I don’t share my political or religious perspectives at work; I never have”, asserted my experienced professorial colleague over an informal coffee. “A bit of shame, but kind of admirable, right?”, I thought.

I recalled a politics lecturer during my time as an undergraduate, who, like seemingly most of that generation of academics (1990s-00s), believed in impartiality and explicitly stated his liberal neutrality when presenting challenging topics: may the best arguments win. The problem was that through reading his online bio and finding his works in the library, one could very quickly discern his political and philosophical leanings!

When I began teaching philosophy at the same university a few years later, I too attempted to feign neutrality; neither sharing my political nor religious leanings, nor ethnic or cultural heritage. It wasn’t the done thing. Autobiography and self-disclosure had no place in the philosophy seminar room.

I’ve since thawed. I’m now leaning far more towards disclosure than when I started teaching. I long held neutral impartiality as the gold standard of instruction, whereby challenging – and perhaps controversial – topics were discussed, but the educator held the space for students to explore perspectives, without sharing their own. This, while often the received wisdom, and certainly well-intentioned, is, I now reflect, limited.

For an academic to be teaching on a module, especially if they’ve created it, means they’re very likely to be published in that field of inquiry. Engaged students will find such materials, understand their lecturer’s perspectives, and recognise when they’re playing devil’s advocate in sessions. Furthermore, given that we teach face to face, and not in confession booths, the visibility of us as lecturers often speaks volumes; students will make an array of assumptions. For example, if in a session led by the university’s chaplain, it’s safe for students to assume that they’re a member of the Church of England.

Kelly’s heuristic quartet

There is a case to be argued for “committed impartiality” as per Social Scientist Thomas Kelly’s (1986) heuristic quartet:
- Exclusive neutrality: The educator takes a neutral position and eschews any potentially controversial issues; i.e. appropriate in a school context, but too reductive for HE.
- Exclusive partiality: The educator takes a biased position; i.e. traditionally a big no no. Think here of educators who use their classes to enact their activism.
- Neutral impartiality: The educator is impartial and neutral, encouraging students to explore controversial issues; i.e. the gold standard of HE instruction based on received wisdom.
- Committed impartiality: The educator takes a biased position while also being impartial; i.e. seen with scepticism by those who practise neutral impartiality. This is a potentially slippery slope into exclusive partiality.
While referring principally to the teaching of “controversial” topics in school education, I think the quartet can be helpfully adapted to fit the context of contemporary HE teaching in the social sciences and humanities. Kelly claimed that owing to its contradictory position, “committed impartiality” is the most defensible course of action for educators to engage in teaching controversial issues. This is because it requires the educator to put their cards on the table and encourage debate without claiming an unbiased standpoint.

Wading

When discussing loaded issues such as race, sexuality and religious perspectives, perhaps this is where the received wisdom about steadfastly refusing to disclose shines through and avoids the – especially contemporary – quagmire of a shallow form of identity politics and virtue signalling that can sometimes turn into a form of oppression Olympics? The “disclosure dilemma” is, of course, ultimately a personal, context bound one.

In the context of schools, the issue of disclosure is much more vexed, given that teachers are effectively agents of the state who have a moral duty to avoid prosletysing given the power dynamic of the classroom (I recall the example during COvid-19 of a teacher in Nottinghamshire getting national attention for encouraging students to write letters of frustration to the then PM).

While school curricula are obviously created by groups of individuals with political agendas, in HE we too have areas of expertise, interest, and passion. In an increasingly regulatory framework, the dissemination of our darlings is bound by legislation such as the Equality Act (2010), and The Higher Education Freedom of Speech Act (2023). Furthermore, to adhere to these acts within a localised context, my employer has a university dignity policy, mission statements, and, within my department, enacts the Chatham House Rule. We also provide trigger warnings to create inclusive learning environments.

Tightrope

This discussion has implications for those in the social sciences, especially those who deal, like I do, with explicitly political content (I recognise that the personal is also the political). Of course, navigating the tightrope between committed impartiality and exclusive partiality is tricky. The received wisdom is valuable insofar as it helps the educator to avoid this balancing act. But when the educator has a specialism that speaks to a political issue of the day, it is arguably upon them to do so. For example, in March 2023 I was teaching a session for final year UG students on migration in the context of international education when the Gary Lineker “issue” kicked off. I had a well-informed perspective on that issue, and it linked neatly to the scheduled taught content that day. It’s fair to say that I teetered on that tightrope between committed impartiality and exclusive partiality!

The challenge is not about self-censorship in the service of an apparently noble ideal of neutral impartiality, but enacting personal commitment and setting the groundwork for civic debate. Deciding to disclose may have the intended learning outcome of rapport building, modelling particular behaviours or perspectives, humanising oneself, normalising situations, or problematising a set of affairs; it’s about practising the messy craft of educating, and being open to self-transformation.

Risk aversion

I’m sure others could make equally compelling cases for different positions within, and outside of, Kelly’s heuristic quartet. I think a primary driver behind neutrality is, rather than a noble but impossible quest for untainted discourse, perhaps one of nervousness; nervousness of being seen as doctrinaire or unduly influencing students’ perspectives?

Overall, the disclosing instructor must consider their visibility in terms of gender, age, physical presence, professional titles etc. that starkly reinforce a power imbalance between student and academic, aka judge, jury and executioner in terms of grades and longer-term prospects. Where the stakes are high boldness of speech, disclosing personal leanings in a learning environment are worth the risk.
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March 5, 2025
Teaching Public Speaking Skills for Our Remote Age With MindTap Bongo Present Activities

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I remember that fateful day clearly, back in March 2020, when we were first told “Go home. We’re going remote.” On the way out the door, one of my colleagues said, “This changes everything.” At the time, I thought they were overreacting. My focus was on health and safety. Naively, I thought the COVID-19 pandemic would pass quickly, and we would soon return to normal.

Rarely have I been so wrong about so many things.

As a communication professor for more than thirty years, I assumed public speaking meant speaking in-person, in public. At the beginning of remote learning, I instructed students to present speeches on Zoom in much the same way I had when our classroom was live, in-person. However, after several semesters of trial and error, I finally appreciated the truth of my colleague’s statement. Everything had changed. While many of the skills required for effective public speaking remotely were the same as public speaking in person, teaching additional skills was necessary.

Public speaking skills: critical for career success

Happily, I discovered learning these remote public speaking skills would not only support students’ academic success but would also support their long-term workplace success. According to research in Cengage’s Career Readiness eBook, 98.5% of employers think communication skills are very important. Additionally, LinkedIn ranked communication as No. 1 on their 2024 list of overall most in-demand skills. Ultimately, this is a skill that will only benefit students in the long run. So, how can students hone this skill?

When it comes to public speaking in any environment, practice is always key. Experts often suggest students give practice presentations, paying close attention to things like their body language, tone of voice and breath control. Practicing in front of others can also be tremendously helpful when preparing.

The challenge of incorporating peer feedback skills in remote teaching

Providing constructive feedback is an essential skill for remote public speaking. Teaching my students how to provide constructive feedback had always been an integral part of my in-person public speaking curriculum.

First, I would offer a lesson with guidelines on how to offer constructive feedback. Then, students would be responsible for completing a speech critique form of another student’s presentation. And finally, students would reflect on ways they could improve their performance based on the feedback they received. Research suggests this type of peer review process helps students to develop lifelong skills in assessing and providing feedback to others, while simultaneously equipping them with skills to self-assess and improve their own speeches.

When I had a full class of face-to-face students, integrating these types of peer review experiences into my public speaking curriculum was relatively easy. However, I quickly learned that the remote learning environment presented a new set of peer review challenges. Just recording speeches to a viewing platform wasn’t enough to replicate the learning opportunities of the in-person experience. Ideally, students needed to be able to record their speeches for asynchronous viewing by the instructor and the assigned students, who would then offer written constructive feedback for the presenter and other peer reviewers to consider. These requirements seemed like a tall order but, amazingly, MindTap, Cengage’s online learning platform, provided me with exactly what I needed.

Using MindTap to teach remote public speaking skills

Prior to my public speaking courses shifting to remote learning, I had already been using online MindTap activities to supplement the print versions of my textbooks. After the pandemic, I began to rely more heavily on MindTap activities. I found using MindTap filled in some of what was lost from my students’ in-person experience, keeping them more engaged. Additionally, using the MindTap Bongo Present activities, which are available with many of the Communication Studies eBooks, solved a number of practical dilemmas including how to systematically evaluate their performance.

Present Bongo activities, found in the MindTap learning path, help students become more comfortable with the act of speaking to a camera while being recorded to a screen through a variety of topic-specific, impromptu-style, low-stakes public speaking opportunities.

Present activities can also be used as an effective delivery and evaluation system for more formal public speaking presentations, such as pre-planned informative or persuasive speeches. When students record their speech, in addition to receiving feedback and a grade from me, they can also receive feedback from other class members, either by a rubric-based peer review or live, real-time comments.

Having the option to assign three or more reviewers for each speech provides additional benefits, for both the reviewer and the speaker. As reviewers, students get to see a wider range of work, and as speakers, they get more feedback on their presentations. If multiple reviewers make the same suggestion, a speaker may be more likely to take that suggestion to heart.

The pathway to public speaking success in a remote setting includes setting aside time to rehearse and record presentations and asking colleagues for constructive feedback. In much the same way, MindTap Bongo activities provide students the opportunity to practice their speaking skills, learn from the review/feedback process and, ultimately, to succeed in our remote age.

Written by Sheryll Reichwein, MA, Adjunct Professor of Communication at Cape Cod Community College

Interested in exploring how MindTap Bongo Activities can help your students develop remote public speaking skills effectively?

The post Teaching Public Speaking Skills for Our Remote Age With MindTap Bongo Present Activities appeared first on The Cengage Blog.

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February 28, 2025