Category: Faculty Development

Mentorship Gone Missing: A Blueprint for a Mentorship Program – Faculty Focus

Mentorship is a learning-centered, leadership-driven experience that nurtures the next generation of researchers and clinicians in academia (Rabeeah et al 2022). Faculty mentors in allied health science programs play a crucial role in shaping their mentees’ professional growth and long-term career fulfillment. Mentor-mentee relationships may be faculty and student, or seasoned faculty and junior faculty.

When reflecting on your prior experiences as a mentor or a mentee, ask yourself these questions:

1. Was it a positive experience?

2. Did you experience disparities based on your gender or race as a mentor or mentee?

3. Did you feel you had adequate training to be a mentor, or did your mentor demonstrate they had the skills to support you as a mentee? and

4. How were the mentors and mentees matched?

Many more questions need to be asked to ensure the mentor-mentee relationship and interactions are sufficient for exponential career growth. However, cultivating a positive experience for the mentee, ensuring there are no barriers (i.e., gender or race) for a unique experience, mentor training, and appropriate pairing of mentors and mentees are overarching considerations supported by evidence. (Rabeeah Z, 2022, Stenfors-Hayes T 2011, Mascarenhas F 2019, Bartle et al. 2020, Hill et al 2022, Frei et al 2010)

Problem

Concurrently, established formal mentorship programs are lacking in allied health science education programs for both students and junior faculty. Having formal mentorship programs for faculty to obtain training for mentorship roles and guidelines to support both junior faculty and students is essential for foundational success in academia. (Reibel & Arnett 2025, Mascarenhas F 2019) As a result of the lack of faculty mentors, students may not be getting exposure to avenues beyond clinical practice. This has been identified in dental hygiene education. (Reibel & Arnett 2025) It is suspected that allied health science programs, in general, have similar barriers to developing, implementing, and evaluating research mentorship programs. The goal of this article is to provide a blueprint for allied health programs to establish a framework for initiating a formal mentorship program.

Solution

To develop and implement a formal mentoring program, you need institutional ‘buy-in,’ strategic planning, faculty and students for piloting the program, an assessment metric(s), and a review of the outcomes process to make recommendations to modify the mentorship program. All these aspects depend on institutional support. The first step would be to identify the need for a mentorship program supported by evidence. The next step would be to align a mentorship program with the institution’s strategic plan.

Strategic Planning

Strategic planning is essential for building coalitions among deans, department chairs, program directors, and mentors. It is the deliberate process of setting goals, identifying resources, and establishing priorities. Beginning with a vision and mission statement may serve as the foundation to set goals to execute and then position a mentor program to identify resources, priorities and specific groups or individuals in a coalition of champions.

To build a diverse wide network of champions to support a variety of perspectives, it is important to identify individuals and groups who can contribute a range of resources, are versatile, and have the ability to pivot when changes need to occur. However, it is equally important that the determined coalition of champions are in alignment with the vision and the mission of the mentorship program. A mentorship program coalition of champions may support inclusive and sustainable programing and increase the reach of available resources. Further a mentorship coalition is especially relevant to match appropriate mentors and mentees and provide a unique mentor experience for the mentee for lifelong learning and professional development.

The next aspect of strategic planning is the development and implementation of a pilot program. A mentorship pilot program is needed on a small scale to determine feasibility. Mentorship pilot programs are required to collect outcomes to evaluate for expansion and improvements. Additionally, if funding is required to sustain or build the mentorship program for expansion, these primary outcomes are needed to secure internal or external grant funding.

Allied health science programs may find available resources for a mentorship program through their human resource department, professional organizations, or government agencies. One example of a successful student research mentorship program was published in the Journal of Dental Hygiene in 2025. (Reibel & Arnett 2025) This student research mentorship program partnered with the Writing Enrichment Curriculum to develop assignments and rubrics to improve student writing. (Reibel & Arnett 2025) This student research mentorship program not only implemented standardized assignments and grading rubrics across the curriculum but also resulted in eight student-led peer-reviewed publications between the years 2019-2023. (Reibel & Arnett 2025) Although the strategic plan and resources provided in this publication are specific to an allied oral health science program, (Reibel & Arnett 2025) the information is transferable to any allied health science program for students and can be modified for a faculty mentorship focus.

Conclusion

This blueprint is applicable to formal mentorship for faculty and students. The main framework is transferable and can be modified to match your institution’s strategic plan and goals. It is important to be aware of key components to ensure a humanistic mentor and mentee experience is cultivated. Strategic planning is necessary for developing, implementing, and evaluating a mentorship program. A coalition of champions who share the values of the vision and mission of a formal mentorship program contributes to the overall mentorship program and evaluation of metrics for future modifications and longevity.

Michelle Arnett currently serves as a Tenured Associate Professor in the Department of Primary Dental Care, Division of Dental Hygiene (DH) at the University of Minnesota (UMN) School of Dentistry (SOD). Arnett has 10 years of experience in academics and research at the University of Michigan SOD and the UMN SOD, in addition to 21 years of clinical practice experience. Her areas of research focus are motivational interviewing and periodontology. Arnett has published over 50 peer-review articles and two textbook chapters. Of those 50, 29 are specific her research niche in motivational interviewing and periodontology. The remaining 21 peer-reviewed publications focus on her teaching, student mentee research projects, and faculty and students’ wellness. Arnett has given over 60 presentations; 20 were invited national and international events. In 2023, Arnett received the American Dental Education Association (ADEA) President Centennial Awards for Oral Health Education. This was a one-time award at ADEA’s centennial celebration for an allied dental faculty who has made and continues to make a significant and positive impact on oral health education. Arnett has taught Communications, Periodontology, Leadership, and the Dental Hygiene Care Process: Clinical Application III and IV in the UMN undergraduate DH program and Instructional Strategies, Thesis I and II, and Capstone I-IV in the UMN graduate DH program. Her professional memberships include Sigma Phi Alpha-Nu Chapter, American Dental Hygienists’ Association (ADHA), and the ADEA.

References

Bartle, Emma K., Brandon J. Crivello, Jeri L. Bullock, and Ezinne I. Ogbureke. “Mentorship availability and needs for junior faculty members at the United States and Australian dental schools.” European Journal of Dental Education 24, no. 4 (2020): 790-798.

Frei, Esther, Martina Stamm, and Barbara Buddeberg-Fischer. “Mentoring programs for medical students-a review of the PubMed literature 2000-2008.” BMC medical education 10 (2010): 1-14.

Hill, Sarah EM, Wendy L. Ward, A. Seay, and J. Buzenski. “The nature and evolution of the mentoring relationship in academic health centers.” Journal of clinical psychology in medical settings 29, no. 3

Mascarenhas, Faye, Janice Townsend, Paul Caballero, Qingzhao Yu, and Paul L. Fidel Jr. “Student and faculty perspectives of a faculty‐student mentoring programme in a dental school.” European Journal of Dental Education 23, no. 2 (2019): 184-189.

Rabeeah, Zainulabdeen, Jocelid G. Carreno, Janet S. Kinney, and Marita R. Inglehart. “Career motivation and satisfaction of dental hygiene students in associate versus bachelor degree programs: A national survey.” Journal of Dental Education 86, no. 6 (2022): 649-660.

Reibel, Y.G., Jiang, Z., Arnett, M.C. “Mentor Like Minnesota: Outcomes of an undergraduate research mentorship program.” Journal of Dental Hygiene (Online) 99, no. 2 (2025): 48-54.

Stenfors‐Hayes, T., L. E. Lindgren, and S. Tranaeus. “Perspectives on being a mentor for undergraduate dental students.” European Journal of Dental Education 15, no. 3 (2011): 153-158.

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August 11, 2025
Mentorship Gone Missing: A Blueprint for a Mentorship Program – Faculty Focus

Mentorship is a learning-centered, leadership-driven experience that nurtures the next generation of researchers and clinicians in academia (Rabeeah et al 2022). Faculty mentors in allied health science programs play a crucial role in shaping their mentees’ professional growth and long-term career fulfillment. Mentor-mentee relationships may be faculty and student, or seasoned faculty and junior faculty.

When reflecting on your prior experiences as a mentor or a mentee, ask yourself these questions:

1. Was it a positive experience?

2. Did you experience disparities based on your gender or race as a mentor or mentee?

3. Did you feel you had adequate training to be a mentor, or did your mentor demonstrate they had the skills to support you as a mentee? and

4. How were the mentors and mentees matched?

Many more questions need to be asked to ensure the mentor-mentee relationship and interactions are sufficient for exponential career growth. However, cultivating a positive experience for the mentee, ensuring there are no barriers (i.e., gender or race) for a unique experience, mentor training, and appropriate pairing of mentors and mentees are overarching considerations supported by evidence. (Rabeeah Z, 2022, Stenfors-Hayes T 2011, Mascarenhas F 2019, Bartle et al. 2020, Hill et al 2022, Frei et al 2010)

Problem

Concurrently, established formal mentorship programs are lacking in allied health science education programs for both students and junior faculty. Having formal mentorship programs for faculty to obtain training for mentorship roles and guidelines to support both junior faculty and students is essential for foundational success in academia. (Reibel & Arnett 2025, Mascarenhas F 2019) As a result of the lack of faculty mentors, students may not be getting exposure to avenues beyond clinical practice. This has been identified in dental hygiene education. (Reibel & Arnett 2025) It is suspected that allied health science programs, in general, have similar barriers to developing, implementing, and evaluating research mentorship programs. The goal of this article is to provide a blueprint for allied health programs to establish a framework for initiating a formal mentorship program.

Solution

To develop and implement a formal mentoring program, you need institutional ‘buy-in,’ strategic planning, faculty and students for piloting the program, an assessment metric(s), and a review of the outcomes process to make recommendations to modify the mentorship program. All these aspects depend on institutional support. The first step would be to identify the need for a mentorship program supported by evidence. The next step would be to align a mentorship program with the institution’s strategic plan.

Strategic Planning

Strategic planning is essential for building coalitions among deans, department chairs, program directors, and mentors. It is the deliberate process of setting goals, identifying resources, and establishing priorities. Beginning with a vision and mission statement may serve as the foundation to set goals to execute and then position a mentor program to identify resources, priorities and specific groups or individuals in a coalition of champions.

To build a diverse wide network of champions to support a variety of perspectives, it is important to identify individuals and groups who can contribute a range of resources, are versatile, and have the ability to pivot when changes need to occur. However, it is equally important that the determined coalition of champions are in alignment with the vision and the mission of the mentorship program. A mentorship program coalition of champions may support inclusive and sustainable programing and increase the reach of available resources. Further a mentorship coalition is especially relevant to match appropriate mentors and mentees and provide a unique mentor experience for the mentee for lifelong learning and professional development.

The next aspect of strategic planning is the development and implementation of a pilot program. A mentorship pilot program is needed on a small scale to determine feasibility. Mentorship pilot programs are required to collect outcomes to evaluate for expansion and improvements. Additionally, if funding is required to sustain or build the mentorship program for expansion, these primary outcomes are needed to secure internal or external grant funding.

Allied health science programs may find available resources for a mentorship program through their human resource department, professional organizations, or government agencies. One example of a successful student research mentorship program was published in the Journal of Dental Hygiene in 2025. (Reibel & Arnett 2025) This student research mentorship program partnered with the Writing Enrichment Curriculum to develop assignments and rubrics to improve student writing. (Reibel & Arnett 2025) This student research mentorship program not only implemented standardized assignments and grading rubrics across the curriculum but also resulted in eight student-led peer-reviewed publications between the years 2019-2023. (Reibel & Arnett 2025) Although the strategic plan and resources provided in this publication are specific to an allied oral health science program, (Reibel & Arnett 2025) the information is transferable to any allied health science program for students and can be modified for a faculty mentorship focus.

Conclusion

This blueprint is applicable to formal mentorship for faculty and students. The main framework is transferable and can be modified to match your institution’s strategic plan and goals. It is important to be aware of key components to ensure a humanistic mentor and mentee experience is cultivated. Strategic planning is necessary for developing, implementing, and evaluating a mentorship program. A coalition of champions who share the values of the vision and mission of a formal mentorship program contributes to the overall mentorship program and evaluation of metrics for future modifications and longevity.

Michelle Arnett currently serves as a Tenured Associate Professor in the Department of Primary Dental Care, Division of Dental Hygiene (DH) at the University of Minnesota (UMN) School of Dentistry (SOD). Arnett has 10 years of experience in academics and research at the University of Michigan SOD and the UMN SOD, in addition to 21 years of clinical practice experience. Her areas of research focus are motivational interviewing and periodontology. Arnett has published over 50 peer-review articles and two textbook chapters. Of those 50, 29 are specific her research niche in motivational interviewing and periodontology. The remaining 21 peer-reviewed publications focus on her teaching, student mentee research projects, and faculty and students’ wellness. Arnett has given over 60 presentations; 20 were invited national and international events. In 2023, Arnett received the American Dental Education Association (ADEA) President Centennial Awards for Oral Health Education. This was a one-time award at ADEA’s centennial celebration for an allied dental faculty who has made and continues to make a significant and positive impact on oral health education. Arnett has taught Communications, Periodontology, Leadership, and the Dental Hygiene Care Process: Clinical Application III and IV in the UMN undergraduate DH program and Instructional Strategies, Thesis I and II, and Capstone I-IV in the UMN graduate DH program. Her professional memberships include Sigma Phi Alpha-Nu Chapter, American Dental Hygienists’ Association (ADHA), and the ADEA.

References

Bartle, Emma K., Brandon J. Crivello, Jeri L. Bullock, and Ezinne I. Ogbureke. “Mentorship availability and needs for junior faculty members at the United States and Australian dental schools.” European Journal of Dental Education 24, no. 4 (2020): 790-798.

Frei, Esther, Martina Stamm, and Barbara Buddeberg-Fischer. “Mentoring programs for medical students-a review of the PubMed literature 2000-2008.” BMC medical education 10 (2010): 1-14.

Hill, Sarah EM, Wendy L. Ward, A. Seay, and J. Buzenski. “The nature and evolution of the mentoring relationship in academic health centers.” Journal of clinical psychology in medical settings 29, no. 3

Mascarenhas, Faye, Janice Townsend, Paul Caballero, Qingzhao Yu, and Paul L. Fidel Jr. “Student and faculty perspectives of a faculty‐student mentoring programme in a dental school.” European Journal of Dental Education 23, no. 2 (2019): 184-189.

Rabeeah, Zainulabdeen, Jocelid G. Carreno, Janet S. Kinney, and Marita R. Inglehart. “Career motivation and satisfaction of dental hygiene students in associate versus bachelor degree programs: A national survey.” Journal of Dental Education 86, no. 6 (2022): 649-660.

Reibel, Y.G., Jiang, Z., Arnett, M.C. “Mentor Like Minnesota: Outcomes of an undergraduate research mentorship program.” Journal of Dental Hygiene (Online) 99, no. 2 (2025): 48-54.

Stenfors‐Hayes, T., L. E. Lindgren, and S. Tranaeus. “Perspectives on being a mentor for undergraduate dental students.” European Journal of Dental Education 15, no. 3 (2011): 153-158.

Source link

August 11, 2025
Job Satisfaction and Retention in Higher Education – Faculty Focus
Have you ever felt the weight of being overworked and underpaid? Do you find yourself questioning your value in your workplace? Are you contemplating exploring new employment opportunities in the near future? If these thoughts resonate, knowing you are not alone in this journey is important. Many of your peers share these concerns.

The CUPA-HR 2023 Higher Education Employee Retention Survey (ERS) is not just a study; it’s a comprehensive collection of your experiences and opinions. It gathered data from 4,783 higher education employees across 539 institutions, including administrators, professionals, and non-exempt staff from various departments. This survey is valuable for understanding the job satisfaction and retention challenges you and your peers face in higher education (Bichsel et al., 2023).

The 2023 survey asked questions in the following areas:
- Likelihood of looking for other employment opportunities in the near future
- Reasons for looking for other employment
- Remote work policies and opportunities
- Work performed beyond normal hours and duties (overwork)
- Satisfaction with benefits
- Well-being and satisfaction with the job environment
- Retention incentives experienced in the past year
- Challenges for supervisors
- Demographic questions on gender, race/ethnicity, and age
- Characteristics of the employee’s position (Bichsel et al., 2023)
The survey’s analyses provided critical insights into the proportion of our higher education workforce at risk of leaving, the reasons why employees are considering other job opportunities, and the underlying factors contributing to their desire to leave. These findings are crucial in addressing our shared concerns. (Bichsel et al., 2023)

The survey results are as follows: one-third (33%) of higher ed employees are very likely or likely to look for new employment opportunities in the next year, which is unchanged from last year. This indicates that retention remains a significant and urgent challenge in higher ed. The area with the most acute retention challenge is student affairs, where 39% of employees surveyed say they are likely or very likely to look for other employment opportunities within the next year (Bichsel et al., 2023)

*A graph of reasons employees are seeking new opportunities was obtained from CUPA-HR’s 2023 survey data. (Bichsel et al., 2023)

A notable aspect of dissatisfaction revolves around their institution’s support for their professional growth. Almost half of employees (44%) express disagreement when asked whether they have opportunities for advancement at their institution, and around one-third (34%) disagree that their institution invests in their career development. Moreover, more than one-fourth of respondents (28%) disagree that their institution’s leaders show they care about their mental health and well-being. (Bichsel et al., 2023)

According to CUPA-HR research and key findings for 2023-2024, across higher education, employees are still paid less in inflation-adjusted dollars than in 2019-20 (pre-pandemic). (CUPA, 2024)

*A screenshot of a graph of Annual Pay Increases by Position Type was obtained from CUPA-HR research and key findings for 2023-2024. (CUPA, 2024)

In a well-run organization, these nuts and bolts that shape compensation are routinely checked and updated to stay competitive and achieve strategic goals. However, for many colleges, compensation practices have become the ‘deferred maintenance’ of the human resources world. This term refers to the practice of postponing necessary maintenance, often due to a need for more funds or resources. In the context of compensation, these practices must be regularly reviewed and updated, leading to issues such as stagnant salaries, late paychecks, inaccurate titles, and confusion over how pay is pegged to performance. (McClure, 2024)

Despite the availability of rich data backed by years of research, institutions are still mainly lagging in implementing standard-raised pay scales, resulting in higher attrition levels.

The survey’s findings are a call to action. Senior management and leadership must approach the situation holistically and use the data to implement a solution swiftly. This is crucial to prevent the loss of trained, qualified, and high-performing employees, a loss that higher educational institutions cannot afford!

Ranjitha Rao is the Budget/Financial Analyst Manager at the College of Nursing at Texas Woman’s University. She is dedicated to supporting academic and administrative goals through financial oversight. As an active member of the TWU Staff Council, she fosters a spirit of unified community among staff members and provides opportunities for their democratic representation. Through her involvement in the Staff Council, she promotes a positive and collaborative work environment and serves as a representative advisory member, presenting recommendations to university leadership. Ranjitha is also committed to fostering healthy workspaces, ensuring faculty, staff, and students thrive in a supportive and productive environment. Ranjitha holds a background in engineering and is currently a Ph.D. student focusing on leadership in higher education. She also holds a Master’s in Business Administration, with an emphasis in Accounting and Management. Additionally, as an adjunct, Ranjitha has taught first-year incoming classes, focusing on curriculum and strategic success, to help students transition smoothly into their academic journeys, along with accounting and healthcare administration classes for undergraduates. Ranjitha’s research interests include competency-based education, workforce development, leadership, management, and financial well-being in higher education. In her free time, Ranjitha enjoys exploring financial trends, participating in community events, and contributing to initiatives that promote financial literacy and education.

References

Bichsel, J., Fuesting, M., Tubbs, D. & Schneider, J. (2023, September). THE CUPA-HR 2023 Higher Education Employee Retention Survey. CUPA-HR. https://www.cupahr.org/surveys/research-briefs/higher-ed-employee-retention-survey-findings-september-2023/

CUPA. (2024). Workforce Pay Increases. https://www.cupahr.org/surveys/workforce-data/workforce-pay-increases/

McClure, K. (2024). Your Pay is Terrible? You’re not alone. The Chronicle of Higher Education, https://www.chronicle.com/article/your-pay-is-terrible-youre-not-alone
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May 28, 2025
How You Can Habituate the Circular Model of Reflection: Before-Action, During-Action, After-Action, and Beyond-Action – Faculty Focus

As scholar-teachers, we know that the habit of reflection can be a powerful tool for workplace learning and performance, which can separate high-performance professionals from mediocre ones.

Notedly, when educators engage in reflection, it is usually in the form of John Dewey: “We do not learn from experience…we learn from reflecting on experience.” This type of reflection is called after-action or on-action. However, often overlooked is that there are other forms of reflection. Further, reflection-after-action may not have the highest return of effort; or be the main task that drives the 20% of the effort that could result in the 80% of desired outcomes, the so-called 80/20 rule of which has been integrated in higher education.

That is, according to Donald Schon, who has written several of the most remarkable texts on reflection in the past several decades, educational practitioners should sift their reflection through during-action (or in-action), rather than primarily relying on after the action.

In contrast to Dewey, Schon’s reflection-during-action explicitly aligns with the idea that there is intellectual rigor in professional practice, such as those of us who are scholar-teachers. Here, Schon demonstrates that intellectual rigor lies in our knowing-in-action, which is tacit and gained through doing and experimenting. However, we are often unaware of tacit knowledge that underlies our activities and actions, especially when things do not go as planned.

Therefore, Schon has designed reflection-during-action in which we learn to frame and reframe unexpected outcomes, “talking-back” and “back-talking” to ourselves while teaching simultaneously. In turn, our actions and reactions can adequately address those unexpected classroom issues in real time. By implication, if the focal reflection is towards after the action, the classroom can become a bottleneck that hinders learning flows and creates more bad teaching days.

For Schon, while reflection-during-action is the most difficult to do, it has the highest RoE™ among the different types. Yet, Schon does not dismiss the importance of setting and reflecting a direction before the action in order to have a strategic plan before entering the classroom. Additionally, when leaving the classroom, the reflection-during-the action becomes the primary material for reflection after the action.

How I Approach the Circular Model of Reflection

In general, Schon’s model of reflection is practicable, where unexpected class issues can be identified and sufficiently solved immediately; together with before the action and after the action, the problem-setting and problem-solving can be iterated until desired results come about. Not to be overlooked is when educators circulate their knowing-in-action with and for others, this signifies beyond-action, cultivating double-loop learning for all to benefit from.

In experimenting with Schon’s reflective practices, I see the importance of connecting and sequencing the four types of reflection, as this has been implemented in practitioner fields such as nursing education and medical education. In particular, I have utilized this model when I teach a new course for the first time, improving an existing course, and/or experimenting with new teaching practices.

During the Covid-19 pandemic, I set a before-action directive in my international business for students to engage in a virtual consulting-based learning. This was done with one of the biggest US medical suppliers, which had to resolve a supply-chain bottleneck of importing medical supplies to American hospitals. The before-action directive included the anticipation of a good deal of messiness and muddling through during classes.

In the above context, I saw my pandemic classroom as a laboratory to prototype and test new pedagogical moves, and where unexpected issues are expected to come to light. Thus, I paid attention and reflected during the action in order to construct adjustments, while doing hybrid-teaching at the same time. Through reflection-after-action I then take a step back and “replay” why, how, and what effects did adjustments have; whether how I could handle those adjustments better; and whether those adjustments change my way of knowing. I then crafted those takeaways into my reflection-before-action for the next class.

In context of the 80/20 rule and its variation, I estimated that my effort in reflection during-action for the above was about 20 percent; and in which about 50 percent of the messiness and unexpected surprises were adequately addressed. Whereas my effort in reflection after-action was about 10 percent and with results about 20 percent, while before-action was about 5 percent with 10 percent in terms of results.

I see the 80/20 rule has an on-going process in which the next time I teach the same course, my goal is to move towards achieving 90% of desired outcomes. As a matter of good practice, I wrote up a Faculty Focus article, sharing my experiences of reflective practice through beyond-action.

Why You Should Engage in the Circular Model of Reflection

By some accounts, Schon’s reflective practice has ushered and advanced that professional practitioners such as scholar-teachers exhibit a form of intelligence, a complex form of professional creativity. As scientists produce new knowledge in their labs, Schon believes that practitioners like scholar-teachers should generate their knowing-in-action –  connecting knowledge generation, best practices, and administration policy.

“When someone reflects in action,” Don Schon states, “he becomes a researcher in the practice context” (Schön, 1983, p.68). For scholar-teachers, I interpret this to mean that we learn more about good teaching as we teach, and even more so when we deliberately integrate reflective practice to generate knowledge for ourselves and for our profession.

When we engage in the circular model of reflection – before-action, during-action, after-action, and beyond-action – we are explicitly creating new data. In turn, we can immediately utilize new information to further our faculty assessment reports, professional development, academic writings, and research publication.

If “we teach who we are,” the circular model of reflection can inform us for our espoused theory (i.e. we say what we do) and our theory in action (i.e. what we actually do). That is, when do make our reflective practice ‘modus operandi,’ we declare our commitment in developing our professional knowledge and artistry.

However, there is an ongoing criticism of the circular model of reflection, where reflection-during-action is thought to be too difficult for scholar-teachers; and there aren’t enough quality examples of when and how to do it in higher education. Interestingly, Schon would probably agree with the above. Ideally, faculty development programs could promote and illustrate that reflection during the action can be learned. That being said, Schon’s reflection-during-action is indeed being practiced in nursing education, medical education, acting schools, and professional sports.

How You Can Apply the Circular Model of Reflection

As mentioned earlier, reflection can be done in four ways: before-action, during-action, after-action, and beyond-action. In contrast to conventional practice of primarily reflecting after the action or significantly sifting to Schon’s reflection during the action, engage in all four types of reflection. First, decide what class is appropriate, such as a course that you expect a good deal of unexpected issues and problems due to the newness of teaching the course topics; experimenting new curriculum materials; new pedagogical approaches to a course; or improving and addressing student course evaluation and student feedback.

Before the action or before the lecture, set directive goals for your action, including naming the purpose and intention for that lecture along with anticipation of surprises and struggles that could emerge. Write them down and have it with you as you start the lecture.

During the lecture, focus on reflection-during-action. That is, while you are lecturing on the content knowledge, have a reflective conversation with yourself (i.e., back-talk and talk-back) on whether the delivery of the content is aligned with your directive goals. When blind spots and shortcomings arise, pay attention to how you reframe them in order to get back on track. When your class ends, write down the during-action reflection as soon as possible.

After the lecture, step back before assessing your reflection notes that you written down. Here, you could significantly benefit by recording your class. When you reflect after the action, see it as replaying or “watching film” of your teaching. Focus on the knowing-in-action, so that if a similar situation occurs, you can talk-back to yourself and reframe the situation more effectively. Write them down for the next class, including adjustment needed for before-action and during-action.

For beyond-action or when the course is over, assess the efficiency of the above reflective practices through the 80/20 rule. That is, estimate the percentage of effort for each reflection, as well as how the effort of each affected the overall desired outcomes. Organize the materials related to the reflective practices and embed them in your faculty assessment report.

Lastly, determine and share with others whether you would use the above reflective practices again.

Conclusion

Overall, the circular model of reflection is designed and promoted as a means to advance professional practitioners, including scholar-teachers. In particular, scholar-teachers can develop and exhibit a form of intelligence and a complex form of creativity. So that they are well equipped to adequately make moment-to-moment decision making and in-time adjustment during class.

Long Le is an Associate Teaching Professor in Management & Entrepreneurship at Santa Clara University. He is also a Faculty Fellow for Collaborative Development for Innovative Teaching.

References

Argyris, C. (1977). “Double Loop Learning in Organizations,” Harvard Business Review.

Baily, J. and S. Rehamn (2022). “Don’t Underestimate the Power of Self-Reflection,” Harvard Business Review.

Dewey, J. (1938, reprinted 1997). Experience and Education. New York: Touchstone.

Edwards, S. (2017). “Reflecting Differently. New dimensions: Reflection-Before-Action and Reflection-Beyond-Action,” International Practice Development Journal 7 (1) [2].

Lurie, S. (2015). “Boost Your Efficiency With the 80/20 Rule,” Inside Higher Ed, Feb. 3.

Lynch, G. (2014). “What We Can Learn from a Bad Day of Teaching,” Faculty Focus, May 12.

Munby, H. (1989). “Reflection-In-Action and Reflection-On-Action,” Current Issues in Education, Volume 9, Fall.

Russel, T. (2005). “Can Reflective Practice be Taught?,” Reflective Practice, 6(2)

Schön, D. (1983). The Reflective Practitioner: How Professionals Think in Action. Guildford, UK: Arena.

Schön, D. (1991). The Reflective Turn: Case Studies in and One Educational Practice. New York: Teachers Press, Columbia University.

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April 25, 2025
How You Can Habituate the Circular Model of Reflection: Before-Action, During-Action, After-Action, and Beyond-Action – Faculty Focus

As scholar-teachers, we know that the habit of reflection can be a powerful tool for workplace learning and performance, which can separate high-performance professionals from mediocre ones.

Notedly, when educators engage in reflection, it is usually in the form of John Dewey: “We do not learn from experience…we learn from reflecting on experience.” This type of reflection is called after-action or on-action. However, often overlooked is that there are other forms of reflection. Further, reflection-after-action may not have the highest return of effort; or be the main task that drives the 20% of the effort that could result in the 80% of desired outcomes, the so-called 80/20 rule of which has been integrated in higher education.

That is, according to Donald Schon, who has written several of the most remarkable texts on reflection in the past several decades, educational practitioners should sift their reflection through during-action (or in-action), rather than primarily relying on after the action.

In contrast to Dewey, Schon’s reflection-during-action explicitly aligns with the idea that there is intellectual rigor in professional practice, such as those of us who are scholar-teachers. Here, Schon demonstrates that intellectual rigor lies in our knowing-in-action, which is tacit and gained through doing and experimenting. However, we are often unaware of tacit knowledge that underlies our activities and actions, especially when things do not go as planned.

Therefore, Schon has designed reflection-during-action in which we learn to frame and reframe unexpected outcomes, “talking-back” and “back-talking” to ourselves while teaching simultaneously. In turn, our actions and reactions can adequately address those unexpected classroom issues in real time. By implication, if the focal reflection is towards after the action, the classroom can become a bottleneck that hinders learning flows and creates more bad teaching days.

For Schon, while reflection-during-action is the most difficult to do, it has the highest RoE™ among the different types. Yet, Schon does not dismiss the importance of setting and reflecting a direction before the action in order to have a strategic plan before entering the classroom. Additionally, when leaving the classroom, the reflection-during-the action becomes the primary material for reflection after the action.

How I Approach the Circular Model of Reflection

In general, Schon’s model of reflection is practicable, where unexpected class issues can be identified and sufficiently solved immediately; together with before the action and after the action, the problem-setting and problem-solving can be iterated until desired results come about. Not to be overlooked is when educators circulate their knowing-in-action with and for others, this signifies beyond-action, cultivating double-loop learning for all to benefit from.

In experimenting with Schon’s reflective practices, I see the importance of connecting and sequencing the four types of reflection, as this has been implemented in practitioner fields such as nursing education and medical education. In particular, I have utilized this model when I teach a new course for the first time, improving an existing course, and/or experimenting with new teaching practices.

During the Covid-19 pandemic, I set a before-action directive in my international business for students to engage in a virtual consulting-based learning. This was done with one of the biggest US medical suppliers, which had to resolve a supply-chain bottleneck of importing medical supplies to American hospitals. The before-action directive included the anticipation of a good deal of messiness and muddling through during classes.

In the above context, I saw my pandemic classroom as a laboratory to prototype and test new pedagogical moves, and where unexpected issues are expected to come to light. Thus, I paid attention and reflected during the action in order to construct adjustments, while doing hybrid-teaching at the same time. Through reflection-after-action I then take a step back and “replay” why, how, and what effects did adjustments have; whether how I could handle those adjustments better; and whether those adjustments change my way of knowing. I then crafted those takeaways into my reflection-before-action for the next class.

In context of the 80/20 rule and its variation, I estimated that my effort in reflection during-action for the above was about 20 percent; and in which about 50 percent of the messiness and unexpected surprises were adequately addressed. Whereas my effort in reflection after-action was about 10 percent and with results about 20 percent, while before-action was about 5 percent with 10 percent in terms of results.

I see the 80/20 rule has an on-going process in which the next time I teach the same course, my goal is to move towards achieving 90% of desired outcomes. As a matter of good practice, I wrote up a Faculty Focus article, sharing my experiences of reflective practice through beyond-action.

Why You Should Engage in the Circular Model of Reflection

By some accounts, Schon’s reflective practice has ushered and advanced that professional practitioners such as scholar-teachers exhibit a form of intelligence, a complex form of professional creativity. As scientists produce new knowledge in their labs, Schon believes that practitioners like scholar-teachers should generate their knowing-in-action –  connecting knowledge generation, best practices, and administration policy.

“When someone reflects in action,” Don Schon states, “he becomes a researcher in the practice context” (Schön, 1983, p.68). For scholar-teachers, I interpret this to mean that we learn more about good teaching as we teach, and even more so when we deliberately integrate reflective practice to generate knowledge for ourselves and for our profession.

When we engage in the circular model of reflection – before-action, during-action, after-action, and beyond-action – we are explicitly creating new data. In turn, we can immediately utilize new information to further our faculty assessment reports, professional development, academic writings, and research publication.

If “we teach who we are,” the circular model of reflection can inform us for our espoused theory (i.e. we say what we do) and our theory in action (i.e. what we actually do). That is, when do make our reflective practice ‘modus operandi,’ we declare our commitment in developing our professional knowledge and artistry.

However, there is an ongoing criticism of the circular model of reflection, where reflection-during-action is thought to be too difficult for scholar-teachers; and there aren’t enough quality examples of when and how to do it in higher education. Interestingly, Schon would probably agree with the above. Ideally, faculty development programs could promote and illustrate that reflection during the action can be learned. That being said, Schon’s reflection-during-action is indeed being practiced in nursing education, medical education, acting schools, and professional sports.

How You Can Apply the Circular Model of Reflection

As mentioned earlier, reflection can be done in four ways: before-action, during-action, after-action, and beyond-action. In contrast to conventional practice of primarily reflecting after the action or significantly sifting to Schon’s reflection during the action, engage in all four types of reflection. First, decide what class is appropriate, such as a course that you expect a good deal of unexpected issues and problems due to the newness of teaching the course topics; experimenting new curriculum materials; new pedagogical approaches to a course; or improving and addressing student course evaluation and student feedback.

Before the action or before the lecture, set directive goals for your action, including naming the purpose and intention for that lecture along with anticipation of surprises and struggles that could emerge. Write them down and have it with you as you start the lecture.

During the lecture, focus on reflection-during-action. That is, while you are lecturing on the content knowledge, have a reflective conversation with yourself (i.e., back-talk and talk-back) on whether the delivery of the content is aligned with your directive goals. When blind spots and shortcomings arise, pay attention to how you reframe them in order to get back on track. When your class ends, write down the during-action reflection as soon as possible.

After the lecture, step back before assessing your reflection notes that you written down. Here, you could significantly benefit by recording your class. When you reflect after the action, see it as replaying or “watching film” of your teaching. Focus on the knowing-in-action, so that if a similar situation occurs, you can talk-back to yourself and reframe the situation more effectively. Write them down for the next class, including adjustment needed for before-action and during-action.

For beyond-action or when the course is over, assess the efficiency of the above reflective practices through the 80/20 rule. That is, estimate the percentage of effort for each reflection, as well as how the effort of each affected the overall desired outcomes. Organize the materials related to the reflective practices and embed them in your faculty assessment report.

Lastly, determine and share with others whether you would use the above reflective practices again.

Conclusion

Overall, the circular model of reflection is designed and promoted as a means to advance professional practitioners, including scholar-teachers. In particular, scholar-teachers can develop and exhibit a form of intelligence and a complex form of creativity. So that they are well equipped to adequately make moment-to-moment decision making and in-time adjustment during class.

Long Le is an Associate Teaching Professor in Management & Entrepreneurship at Santa Clara University. He is also a Faculty Fellow for Collaborative Development for Innovative Teaching.

References

Argyris, C. (1977). “Double Loop Learning in Organizations,” Harvard Business Review.

Baily, J. and S. Rehamn (2022). “Don’t Underestimate the Power of Self-Reflection,” Harvard Business Review.

Dewey, J. (1938, reprinted 1997). Experience and Education. New York: Touchstone.

Edwards, S. (2017). “Reflecting Differently. New dimensions: Reflection-Before-Action and Reflection-Beyond-Action,” International Practice Development Journal 7 (1) [2].

Lurie, S. (2015). “Boost Your Efficiency With the 80/20 Rule,” Inside Higher Ed, Feb. 3.

Lynch, G. (2014). “What We Can Learn from a Bad Day of Teaching,” Faculty Focus, May 12.

Munby, H. (1989). “Reflection-In-Action and Reflection-On-Action,” Current Issues in Education, Volume 9, Fall.

Russel, T. (2005). “Can Reflective Practice be Taught?,” Reflective Practice, 6(2)

Schön, D. (1983). The Reflective Practitioner: How Professionals Think in Action. Guildford, UK: Arena.

Schön, D. (1991). The Reflective Turn: Case Studies in and One Educational Practice. New York: Teachers Press, Columbia University.

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April 25, 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.
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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
Source link
March 31, 2025
Running a Workshop: Guidelines for Engagement and Impact – Faculty Focus
Most of us have the experience of registering for a workshop only to discover that it is a lecture or (worse) an advertisement for the presenter. A genuine workshop aims to empower the participants such that they leave with new ideas and skills.

What follows are some suggested principles we have prepared for workshop facilitators that move beyond a “talkfest” to genuine learning.

Principle 1: Come early and “own” the learning space.

You need to give plenty of time to ensuring the room is arranged to facilitate quality discussion and learning. Make sure that all the technology is working. Greet people as they enter to let them know they are valued and welcome.

Principle 2: A workshop is not a lecture.

The word “workshop” implies that work will be done by the participants. This means the center of attention should be on the students doing work, not on the expert talking about their expertise. Many experts are bad at teaching workshops because they are used to lecturing. A lecture has the spotlight on the speaker, but a workshop has the spotlight on the students. Instead of crafting a message for people to listen to, a good workshop is designed to maximize participant engagement and give the participants the opportunity for guided instruction in doing things. Many workshops are born from lectures, which explains why those workshops are so ineffective and boring.

Principle 3: Small group work is ideal.

Good workshops involve discussion, and discussion is most fruitful in groups of 3-4 members. In smaller groups, every person has the opportunity to express an opinion, and it is far more difficult to remain silent and uninvolved. In groups of five or more, certain individuals will tend to monopolize the discussion, and it is possible for one or more members to remain uninvolved observers. Remember that the two keys to effective learning are involvement and application. Participants are more likely to become involved in a smaller group than in a larger group.

Principle 4: Think in units of three:
1. Explain: Walk the participants through a relatively small piece of information or skill, perhaps (as relevant) providing a model or example that participants can build from.
1. Exercise: Have everyone try to do that thing (while you wander around and help people one on one).
1. Debrief: Discuss the results of the experience: where people got stuck, and what things people learned, or realized they want to learn.
Principle 5: Less is More.

Don’t try to “cover the content”. Address one or two key issues and give time for participants to wrestle with a smaller amount of content but at a deeper level. The result is likely to be more profound and deep in learning.

Principle 6: Keep in mind that discussion takes time.

If you break the class into smaller groups, set a time limit and remind them about halfway through, to ensure that everyone has an opportunity to participate. This gives the group permission to ask anyone who is monopolizing the conversation to finish so that others may have a turn. As teacher/facilitator you should move around the groups, ensuring that they have understood the questions and are progressing through them at an appropriate pace. Your presence will communicate interest and concern, and greatly motivate the students to stay on task and engage with the material.

Principle 7: Carefully design discussion questions in advance.

The best questions need crafting and design. Ensure that every question is divergent and open. Seek to connect not only with the mind but with attitudes and actions.

Principle 8: Stay out of the center.

Participants are coming to learn, and they generally learn from others as much as they do from you. As a general rule you should not talk for more than 5-10 minutes before having the participants reflect and engage. In total the participants should be doing at least half the talking – and more. Facilitation is the name of the game.

Principle 9: Closure is important.

A time of reporting is valuable, but it needs five to ten minutes following the small-group work, and the class structure needs to be planned accordingly. It is generally best not to take extensive responses from each group, as this can become boring and counterproductive. A very effective procedure is to ask each group to contribute its single most important insight. Each insight should be a new contribution to the class reflections rather than the same repetitive information. Only when every group has had an opportunity to share one or two ideas do you solicit additional responses. Sharp and short reporting can give an almost electric dynamism to the feedback process.

Principle 10: Plan to be available after the workshop.

Oftentimes the most significant learning is one-on-one between you and a participant.

Perry Shaw EdD is honorary research associate at Morling College, Australia, and author of Transforming Theological Education.

Orbelina Eguizabal E. PhD is Professor of Christian Higher Education, PhD and EdD Programs in Educational Studies, Talbot School of Theology, Biola University.
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February 10, 2025