Category: student learning

Supporting the Student Researcher: Effective Teaching, Learning, and Engagement Strategies – Faculty Focus

Higher education provides students with the opportunity to participate in a diverse range of academic programs that facilitate further exploration beyond the classroom. Participating in a guided research project offers valuable exposure to discipline-specific inquiry. Under the mentorship of a faculty research advisor, students gain critical skills such as project management, research methods, and early professional development. While the roles and responsibilities of the research advisor can vary depending on the type of research (e.g., human subjects, qualitative, quantitative, mixed methods), there are commonalities in how to best support student researchers. These strategies highlight best practices along with other important considerations for research advisors.

Traditionally, a research advisor guides and oversees student-led research by providing pivotal insight and direction throughout the project (National Academy of Sciences, 2009). The advisor plays a vital role in ensuring that the student follows ethical standards and guidelines. Beyond its formal role, research advisors also serve as mentors who motivate, encourage, and support students as they develop intellectually and professionally. They also create a protected and affirming space for students to be vulnerable in instances of uncertainty as the research project progresses. This dual function as both advisor and mentor has a lasting impact on students’ academic success and career trajectories. Through intentional mentorship and guidance, research advisors help students cultivate scholarly excellence, professional competence, and life skills that extend beyond the academic environment into daily activities.

There are several considerations that may cause faculty members to feel apprehensive about serving as research advisors, especially since these responsibilities extend beyond classroom instruction. This includes increased teaching loads, required committee responsibilities, professional development, community service, and administrative duties (Springer et al., 2023). Balancing these institutional demands can be overwhelming. These expanding roles and expectations contribute significantly to faculty fatigue, often leading to demoralization and persistent stress. Additionally, a student’s aptitude, skills, level of independence, and overall capacity for college-level research can also add to the complexity of serving as a research advisor.

Teaching, Learning, and Engagement Strategies

The advisor role aligns with several teaching and educational philosophies. Notably, Behaviorism (stimuli, reinforcement, and environment) and Essentialism (back to the basics) are all useful. These conceptual and philosophical frameworks are demonstrated in each of the posited strategies through the intentional use of engaging activities and techniques. Also instructive is Jean Piaget’s philosophy, which emphasizes a hands-on approach that focuses on active learning and interaction, which leads to the student’s self-exploration and discovery (Uzun, 2024). Promoting engagement that fosters problem-solving and critical thinking is especially important in the student-researcher-advisor relationship. Serving as a research advisor also requires employing an eclectic approach in relation to teaching and educational philosophies. Throughout each of the following strategies, it is imperative that the student’s learning and each engagement opportunity are meaningful, productive, and place the student researcher at the center of the collaboration. Each of these strategies presents teachable moments not only for students but also for the research advisor. The implementation of each plan to support the student, specifically how and why it is addressed.

Assess Students’ Readiness for Research

Conducting a low-stakes, informal assessment of the students’ interests, strengths, and areas of opportunity is a good starting point. Prior involvement with the student, due to their having taken a course with the faculty, may have already allowed for a preliminary assessment of the student’s preparedness for college-level research. In the absence of experience working with the student, one quick assessment method is to explore the student’s understanding of the research project more deeply. Engage in active listening, ask insightful and probing questions, and allow the process to unfold naturally. This encourages the student to demonstrate what they know about producing a noteworthy project. Students have a wide range of learning styles, competencies, and abilities. The goal is not to judge but to assess through an objective lens. This strategy enables faculty to make informed decisions about the commitment level, existing competencies, and overall student needs when undertaking scholarly research (Sharp, 2002).

Model Behavior and Coach for Success

Engaging students outside of the classroom also allows faculty to demonstrate other skill sets. The research advisor should conduct themselves in a manner that shows students ways to interact with various research stakeholders and how soft skills can be applied across different contexts. Much of the advisor’s time is spent monitoring and supporting the student. It is also imperative that the student is empowered and given the space to work independently, which is vital to students’ metacognition (Howlett, 2021). Research can be daunting; therefore, a key part of the coaching process involves implementing activities and providing affirming feedback that keeps the student engaged and interested. This strategy is essential because it supports students in gaining the requisite proficiencies as a researcher and collaborating with others.

Introduce and Teach Project Management Skills

Prioritizing tasks and effective time management are valuable skills that can enhance a student’s academic, professional, and personal life. Monitoring and evaluating overall goals and objectives promote the success of the research project. A practical approach to this strategy is to assign the student small tasks with specific timelines and deliverables, then provide incremental feedback. Scheduling project meetings to identify immediate tasks and priorities helps students understand and appreciate how to manage projects effectively. Clearly defined due dates, which may sometimes be inflexible, must be communicated (Sharp, 2002). Students are empowered when given relevant information, as well as a clear understanding of each phase of the research project. Adopting this strategy is a value-added benefit for students, as it reduces the likelihood of missed deadlines and critical deliverables throughout the project cycle.

Create a Safe Space for New Experiences and Growth

Naturally, some students may feel intimidated or unsure about how the student-faculty research collaboration will proceed. Uncertainty is expected since this is a new role and experience. It is notably different from previous instructor-student interactions that existed in the classroom. While the faculty advisor anticipates various outcomes, the student also has expectations related to faculty expertise and creativity, as well as benefiting from their full support and constructive criticism (Abbas, 2020). The research advisor must ensure that a safe environment is created for the student to feel empowered to share alternative or completely different viewpoints on various aspects of the research project. Maintaining a safe environment also encourages collaboration, student growth, and overall development. Using this strategy creates an environment of trust, teamwork, and mutual respect.

Finally, regardless of the size or type of institution, service and student engagement activities are viewed positively in higher education. Serving as a research advisor gives faculty the chance to showcase what they do outside the classroom. A large part of their time is dedicated to meeting teaching and learning responsibilities, for which they are evaluated on annually. Great effort is also made to ensure that faculty adequately support students with academic challenges. Taking on the role of research advisor also allows high-achieving students to be engaged. Additionally, this role supports and addresses the needs of students with aspirations for graduate studies, those seeking more challenging opportunities, or who have a general interest in scholarly research. Ultimately, it is a win-win situation for both the student and the faculty member who takes on this important role.

Curtis L. Todd, PhD, is Professor of Social Work in the School of Arts and Sciences at Atlanta Metropolitan State College, where he also provides instruction in Criminal Justice. Quintero J. Moore, MD, is Assistant Professor of Biology in the School of Arts and Sciences at Atlanta Metropolitan State College.

References

Abbas, A., Arrona-Palacios, A., Haruna H., and Alvarez-Sosa, D. 2020. Elements of students’ expectation towards teacher-student research collaboration in higher education. Frontiers in Education Conference (FIE): IEEE.

Howlett, M.A., McWilliams, M.A., Rademacher, K. et al. 2021. Investigating the Effects of Academic Coaching on College Students’ Metacognition. Innovative Higher Education: Springer Nature.

National Academy of Sciences, National Academy of Engineering (US) and Institute of Medicine (US) Committee on Science, Engineering, and Public Policy. (2009). On Being a Scientist: A Guide to Responsible Conduct in Research – Advising and Mentoring. 3rd Edition. National Academies Press: National Academy of Sciences.

Sharp, J.A., Peters, J., & Howard, K. (2002). The Management of a Student Research Project. 3rd Edition. Routledge: Taylor & Frances Group.

Springer, A., Oleksa-Marewska, K., Basinska-Zych, A., Werner, I., and Bialowas, S. (2023). Occupational burnout and chronic fatigue in the work of academic teachers-moderating role of selected health behaviours. National Library of Medicine: PLoS One.

Uzun, Aylin. 2024. Education Based on Piaget’s Theory. Research In Educational Sciences-I: IKSAD.

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September 17, 2025

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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September 8, 2025

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

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

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

Why Students Turn to AI

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

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

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

Process Over Product

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

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

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

Disciplinary Innovation

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

Creative Disciplines

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

Simulating Complexity

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

Modeling Scientific Reasoning

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

Building AI Fluency Across the Curriculum

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Why Students Turn to AI

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

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

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

Process Over Product

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

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

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

Disciplinary Innovation

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

Creative Disciplines

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

Simulating Complexity

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

Modeling Scientific Reasoning

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

Building AI Fluency Across the Curriculum

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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September 5, 2025
$Math is Out, Cake is In: Introducing Students to Rubrics – Faculty Focus$

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

The Context

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

The Illustration

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

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

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

The Discussion

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

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

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

The Application

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

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

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

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

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

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

References

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

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

Suskie, L. (2018). Assessing student learning: A common sense guide. John Wiley & Sons, Incorporated.
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August 27, 2025
Transforming Classroom Discussions with Communication Practices from Health Coaching – Faculty Focus
Classroom discussions are among the most enriching aspects of higher education, offering students opportunities to engage with peers, deepen their understanding of course material, and draw meaningful connections between concepts. For instructors, these discussions serve as a powerful tool to foster collaboration, assess comprehension, and create a more dynamic and inclusive learning environment. Yet, leading effective discussions can be challenging, especially when students are hesitant to participate or when conversations lack depth and substance.

The field of health coaching—where communication is central to guiding clients toward greater self-awareness and behavior change—offers a valuable framework for enhancing classroom dialogue. Health coaching is a collaborative process that relies on intentional and empathetic communication to help clients explore their motivations, clarify their goals, and take ownership of their progress. By applying key communication strategies from this field, educators can elevate the quality of classroom discussions and create more meaningful interactions with students. This article explores how three core techniques from health coaching — the OARS framework (open-ended questions, affirmations, reflective listening, and summarization), rephrasing, and active listening — can be adapted to the classroom to facilitate deeper, more personally relevant conversations.

The OARS Framework

First and foremost, good discussions begin with good questions. Consider the principles contained in the acronym “OARS” to shape the way you formulate questions and listen to responses, facilitating more interactive and less directive discussions.

O: Open-ended questions: Ask questions that require more than discrete responses. Avoid questions that can be answered with “yes,” “no,” or a number. Instead of asking “Are people motivated solely by monetary incentives?”, consider asking: “Tell us about your sources of motivation,” “Why might people not be motivated when provided an incentive?” and “What, in your experience, have you seen motivate your peers to…?” When used effectively, open-ended questions encourage students to think more deeply and progress in their understanding. They can even prompt students to introduce classroom topics without the instructor ever having to explicitly steer the discussion in a particular direction.

A: Affirmations: Don’t hesitate to provide positive statements that acknowledge a student’s contribution. Affirmations foster respect and support an inclusive teaching climate; however, the affirmations must be genuine and authentic. Students notice when the instructor simply says “Great comment” or “Good question” to a majority of students’ responses. Remember, this isn’t an interrogation but a dialogue. Consider the potential impact of statements like: “Sara, thanks for volunteering to share your thoughts about applications of aerobic energy production in exercise programming,” or “Javier, I really appreciate how your comments reflect Van Gogh’s perspective on his use of sunflowers in his paintings.”

RS: Reflective Listening and Summarization: Reflective listening occurs when the instructor reflects back to the student the core elements of their contribution to the discussion. Reflective listening provides an excellent opportunity for the instructor to confirm their understanding of the student’s comments, as well as directing the conversation forward. In this step in the OARS framework, it may be appropriate to ask either a yes/no or an open-ended question. For example, “John, you’re saying personal accomplishment can be a powerful motivator among mountain athletes, is that right?” or “It seems like something is missing in our understanding of what happens when we introduce financial incentive to these groups. What do you think would happen if…?” When applied to the discussion as a whole, a summary of the main points made from the instructor’s perspective can be a great way to remind students of key points, reinforce the instructor’s informal assessment of student learning, as well as introduce the next topic or item in the lesson plan of the day.

Application Moment: Consider how you might have applied these principles of the OARS framework to a recent group discussion you led in class.

While the OARS framework provides an excellent guideline for how an instructor can improve the overall quality discussions, the following two techniques will provide concrete recommendations on what to say and how to act during the discussion.

Rephrasing

Remember the importance of good questions for good discussions? To take things one step further, consider the technique of rephrasing. To encourage deeper topic exploration, consider restructuring questions by replacing the intended question word (e.g., “who,” “what,” “when,” “where,” “why,” or “how”) with a different one. For example, an instructor may want to ask, “How can we increase motivation?” and might rephrase that question as, “What factors increase motivation?” or “Where do you feel the most/least motivated?” or “Who demonstrates the most/least motivation?” or “Why does motivation ebb and flow?” or “When are you most/least motivated?” By changing the question word, the instructor can steer the discussion to change how students think about the question, perhaps eliciting a list of possible outcomes (e.g. “What are the factors?”) or explore possible mechanisms/antecedents (e.g., “Why might motivation decline in competitive settings?”) or to help the students develop their ability to embody or conceptualize new perspectives (e.g. “How do financial incentives impact different groups or individuals? John, you represent a group of…”). This can provide depth to the conversation, encourage creativity and application of concepts in discussion, and allows for the exploration of a variety of interesting tangents.

Application Moment: Write down a discussion question from your last class instruction. Rephrase the question using each of the common question words (“who”, “what”, “when”, “where”, “why”, “how”). Consider the types of responses each question word will elicit.

Active Listening

Last, but not least, active listening: Often, instructors forget that when a question is asked, a response is on the way. It can be surprisingly difficult to fully engage with student responses, as it requires the instructor to focus on what the student is saying rather than on what they will say next. The instructor’s first job is to focus on what students are saying rather than their (the instructor’s) response. The following strategies promote active listening in the classroom during discussions:
- Provide adequate time for student responses.
- Don’t be afraid of silence. Silence provides you and your students time to think.
- Call on students by their first name when you engage with them.
- Get out from behind your lectern and casually move toward the student who is speaking.
- Nod and use affirmations (e.g., nodding or stating “nice insight, Paul”).
- Paraphrase when appropriate (“What I hear you saying, Paul, is that….”) – see “rephrasing/summarization” under the OARS framework.
Fostering meaningful classroom discussions is both an art and a skill—one that can be significantly enhanced by drawing on the principles of communication in health coaching. By integrating techniques such as the OARS framework, rephrasing, and active listening, instructors can create more engaging, inclusive, and thought-provoking learning environments. These strategies not only encourage deeper student participation but also help build a classroom culture rooted in empathy, curiosity, and mutual respect. As higher education continues to evolve, integrating communication practices from other disciplines can be a powerful means of enriching the educational experience and enhancing student learning.

Maria Newton, PhD, F-AASP, is an Associate Professor in the Department of Health and Kinesiology at the University of Utah. She holds a BA from the University of California, Davis, an MS from California State University, Chico, and a PhD from Purdue University, with specialized training in sport and exercise motivation. Dr. Newton’s research focuses on achievement motivation, specifically how perceptions of the climate influence motivational striving. Her most recent contributions to the literature have focused on how creating a caring and task-involving climate fosters motivation. With over 30 years of experience in higher education, Dr. Newton brings extensive expertise in teaching a variety of courses in diverse formats. Recognized with numerous teaching awards, she is deeply committed to cultivating an inclusive and intellectually stimulating climate that inspires learning and personal growth.

Jefferson Brewer, MS, CSCS, ACSM EP, is an Associate Instructor in the Department of Health and Kinesiology at the University of Utah. He holds a BS in Kinesiology with an emphasis in Exercise Science and an MS in Kinesiology focused on Health and Wellness Coaching, both from the University of Utah. He is currently completing his dissertation for a PhD in Integrated Health Science at Rocky Mountain University of Health Professions. Jefferson’s research centers on metabolism in rock climbers—examining both systemic and localized muscular responses. Before transitioning into academia, he worked in patient care, applying his coaching background and exercise science expertise to improve health outcomes. His experience has shaped a teaching style grounded in interpersonal communication, creating engaging and dynamic learning environments that foster growth, curiosity, and academic success.
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August 22, 2025
Transforming Classroom Discussions with Communication Practices from Health Coaching – Faculty Focus
Classroom discussions are among the most enriching aspects of higher education, offering students opportunities to engage with peers, deepen their understanding of course material, and draw meaningful connections between concepts. For instructors, these discussions serve as a powerful tool to foster collaboration, assess comprehension, and create a more dynamic and inclusive learning environment. Yet, leading effective discussions can be challenging, especially when students are hesitant to participate or when conversations lack depth and substance.

The field of health coaching—where communication is central to guiding clients toward greater self-awareness and behavior change—offers a valuable framework for enhancing classroom dialogue. Health coaching is a collaborative process that relies on intentional and empathetic communication to help clients explore their motivations, clarify their goals, and take ownership of their progress. By applying key communication strategies from this field, educators can elevate the quality of classroom discussions and create more meaningful interactions with students. This article explores how three core techniques from health coaching — the OARS framework (open-ended questions, affirmations, reflective listening, and summarization), rephrasing, and active listening — can be adapted to the classroom to facilitate deeper, more personally relevant conversations.

The OARS Framework

First and foremost, good discussions begin with good questions. Consider the principles contained in the acronym “OARS” to shape the way you formulate questions and listen to responses, facilitating more interactive and less directive discussions.

O: Open-ended questions: Ask questions that require more than discrete responses. Avoid questions that can be answered with “yes,” “no,” or a number. Instead of asking “Are people motivated solely by monetary incentives?”, consider asking: “Tell us about your sources of motivation,” “Why might people not be motivated when provided an incentive?” and “What, in your experience, have you seen motivate your peers to…?” When used effectively, open-ended questions encourage students to think more deeply and progress in their understanding. They can even prompt students to introduce classroom topics without the instructor ever having to explicitly steer the discussion in a particular direction.

A: Affirmations: Don’t hesitate to provide positive statements that acknowledge a student’s contribution. Affirmations foster respect and support an inclusive teaching climate; however, the affirmations must be genuine and authentic. Students notice when the instructor simply says “Great comment” or “Good question” to a majority of students’ responses. Remember, this isn’t an interrogation but a dialogue. Consider the potential impact of statements like: “Sara, thanks for volunteering to share your thoughts about applications of aerobic energy production in exercise programming,” or “Javier, I really appreciate how your comments reflect Van Gogh’s perspective on his use of sunflowers in his paintings.”

RS: Reflective Listening and Summarization: Reflective listening occurs when the instructor reflects back to the student the core elements of their contribution to the discussion. Reflective listening provides an excellent opportunity for the instructor to confirm their understanding of the student’s comments, as well as directing the conversation forward. In this step in the OARS framework, it may be appropriate to ask either a yes/no or an open-ended question. For example, “John, you’re saying personal accomplishment can be a powerful motivator among mountain athletes, is that right?” or “It seems like something is missing in our understanding of what happens when we introduce financial incentive to these groups. What do you think would happen if…?” When applied to the discussion as a whole, a summary of the main points made from the instructor’s perspective can be a great way to remind students of key points, reinforce the instructor’s informal assessment of student learning, as well as introduce the next topic or item in the lesson plan of the day.

Application Moment: Consider how you might have applied these principles of the OARS framework to a recent group discussion you led in class.

While the OARS framework provides an excellent guideline for how an instructor can improve the overall quality discussions, the following two techniques will provide concrete recommendations on what to say and how to act during the discussion.

Rephrasing

Remember the importance of good questions for good discussions? To take things one step further, consider the technique of rephrasing. To encourage deeper topic exploration, consider restructuring questions by replacing the intended question word (e.g., “who,” “what,” “when,” “where,” “why,” or “how”) with a different one. For example, an instructor may want to ask, “How can we increase motivation?” and might rephrase that question as, “What factors increase motivation?” or “Where do you feel the most/least motivated?” or “Who demonstrates the most/least motivation?” or “Why does motivation ebb and flow?” or “When are you most/least motivated?” By changing the question word, the instructor can steer the discussion to change how students think about the question, perhaps eliciting a list of possible outcomes (e.g. “What are the factors?”) or explore possible mechanisms/antecedents (e.g., “Why might motivation decline in competitive settings?”) or to help the students develop their ability to embody or conceptualize new perspectives (e.g. “How do financial incentives impact different groups or individuals? John, you represent a group of…”). This can provide depth to the conversation, encourage creativity and application of concepts in discussion, and allows for the exploration of a variety of interesting tangents.

Application Moment: Write down a discussion question from your last class instruction. Rephrase the question using each of the common question words (“who”, “what”, “when”, “where”, “why”, “how”). Consider the types of responses each question word will elicit.

Active Listening

Last, but not least, active listening: Often, instructors forget that when a question is asked, a response is on the way. It can be surprisingly difficult to fully engage with student responses, as it requires the instructor to focus on what the student is saying rather than on what they will say next. The instructor’s first job is to focus on what students are saying rather than their (the instructor’s) response. The following strategies promote active listening in the classroom during discussions:
- Provide adequate time for student responses.
- Don’t be afraid of silence. Silence provides you and your students time to think.
- Call on students by their first name when you engage with them.
- Get out from behind your lectern and casually move toward the student who is speaking.
- Nod and use affirmations (e.g., nodding or stating “nice insight, Paul”).
- Paraphrase when appropriate (“What I hear you saying, Paul, is that….”) – see “rephrasing/summarization” under the OARS framework.
Fostering meaningful classroom discussions is both an art and a skill—one that can be significantly enhanced by drawing on the principles of communication in health coaching. By integrating techniques such as the OARS framework, rephrasing, and active listening, instructors can create more engaging, inclusive, and thought-provoking learning environments. These strategies not only encourage deeper student participation but also help build a classroom culture rooted in empathy, curiosity, and mutual respect. As higher education continues to evolve, integrating communication practices from other disciplines can be a powerful means of enriching the educational experience and enhancing student learning.

Maria Newton, PhD, F-AASP, is an Associate Professor in the Department of Health and Kinesiology at the University of Utah. She holds a BA from the University of California, Davis, an MS from California State University, Chico, and a PhD from Purdue University, with specialized training in sport and exercise motivation. Dr. Newton’s research focuses on achievement motivation, specifically how perceptions of the climate influence motivational striving. Her most recent contributions to the literature have focused on how creating a caring and task-involving climate fosters motivation. With over 30 years of experience in higher education, Dr. Newton brings extensive expertise in teaching a variety of courses in diverse formats. Recognized with numerous teaching awards, she is deeply committed to cultivating an inclusive and intellectually stimulating climate that inspires learning and personal growth.

Jefferson Brewer, MS, CSCS, ACSM EP, is an Associate Instructor in the Department of Health and Kinesiology at the University of Utah. He holds a BS in Kinesiology with an emphasis in Exercise Science and an MS in Kinesiology focused on Health and Wellness Coaching, both from the University of Utah. He is currently completing his dissertation for a PhD in Integrated Health Science at Rocky Mountain University of Health Professions. Jefferson’s research centers on metabolism in rock climbers—examining both systemic and localized muscular responses. Before transitioning into academia, he worked in patient care, applying his coaching background and exercise science expertise to improve health outcomes. His experience has shaped a teaching style grounded in interpersonal communication, creating engaging and dynamic learning environments that foster growth, curiosity, and academic success.
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August 22, 2025
Beyond Digital Literacy: Cultivating “Meta AI” Skills in Students and Faculty – Faculty Focus
Imagine a classroom where AI helps students learn more effectively and faculty have more time to focus on what they do best. That future is closer than you think, thanks to the rise of generative AI. Higher education professionals find themselves in a new world with these emerging tools and their capacity to dramatically change how everyone thinks and learns. There are crucial questions arising on the appropriateness of AI and their impact on thinking, creativity, and intellectual property.

Unlike many instructors, the students in our classrooms are “digital natives” (Prensky, 2001) who have known, used, and relied on technology their whole lives. Often, however, this leads to faulty assumptions about what these students know and can do. Although they are typically fluent and comfortable with technology tools, they are not always able to be metacognitive about how and why they use them and how that use can benefit or impede their learning.

Additionally, generative artificial intelligence cannot be viewed as just another technology “tool.” Its breadth of use is unlike any prior technological development. Unfettered and uncritical use of generative AI by students will certainly affect learning gains and outcomes. We cannot assume that students will learn how to ethically and critically use AI without overt instruction and modeling. As a result, responsibility lies with all instructors to consider how and when they can allow students to use AI in their coursework. Instructors should model what that looks like, what tools are most reliable, and how students can cite and acknowledge AI use. Only through discipline-specific modeling can students gain the “meta AI” ability to make future determinations about responsible use of the tools.

Initial data suggests that faculty and students are experiencing ambiguity and need training to fully understand AI (Petricini et al., 2024). Once faculty comprehend the possibilities of this technology, they can, in turn, model and teach appropriate use to their students. Similar to metacognition, where individuals monitor and manage their own cognitive processes, higher education has a responsibility to help students develop meta AI skills to monitor and manage AI usage. This responsibility presents both opportunities and concerns. As instructors navigate those with a balance of curiosity and humility, some examples may help.

Instructors can:
- Experiment with using AI as a tool to give student feedback. Upload assignment instructions, a rubric, and sample work, and ask AI to provide feedback.
- Explore different prompts and the range of output it generates.
- Think like a student to assess the appropriateness of the feedback and its value to student learning.
- Put the onus on the student. Ask students to explain a concept they are learning and to provide an example to an AI tool and allow the AI to provide feedback.
- Evaluate how well AI supports student understanding. Students can also share the output with faculty thus providing another opportunity for faculty to “see” student thinking and to provide feedback.
After experimenting, bring this experience into the classroom and discuss with students what you learned, where it can be helpful, and areas to avoid using AI. Overtly model how you used it and your evaluation of that use. When appropriate, consider creating a discipline-specific guide for fellow faculty and students on what generative AI is, when to use it, and its potential and drawbacks in your field of study.

Another avenue to developing meta AI skills is to view it from a lens of scholarly teaching. Are there aspects of your teaching that can be improved by its use? How can AI be used to save time and provide valuable information to support learning? AI can be integrated into large-enrollment courses to provide immediate and ongoing feedback on assignments that are extremely time-consuming for one professor to grade. In professional programs, AI can be designed to simulate patients, allowing students to practice initial communication skills prior to their first clinical experiences. In online courses, these tools can be created to simulate classmates, and online students can have real-time discussions or engage in other active learning techniques such as think, pair, share that were not possible before. Modeling how decisions are made regarding the use and integration of AI is a first step for novice users in developing meta ai skills.

Finally, it is imperative that faculty work actively to identify and address when students use AI tools in unauthorized ways. If an instructor disallows AI and students use it anyway with no comment or consequence, they receive the message that AI-generated work is undetectable and acceptable. Any feedback the instructor gives on that work is not feedback on the students’ knowledge and abilities, yet students can misinterpret that feedback to mean that the AI use was an appropriate and successful choice. This is counter to the goal of meta AI and helping students understand how to ethically and responsibly use AI without interfering with their own learning.

As faculty proceed through this new era of education, we must both challenge ourselves to learn, grow, and adapt to the possibilities of generative AI while also relying on what we know about learning and student success. To kickstart this journey, consider taking the following initial steps: Attend workshops or training sessions on AI technology, collaborate with colleagues to share insights and strategies, and integrate AI tools into your curriculum incrementally. As students move through the education process and into the world of work, they must develop intrinsic motivation and metacognitive skills to maximize their chances of success. Those metacognitive skills now must also embrace a meta AI ability so that our future professionals can harness the tools in responsible ways that don’t interfere with all they need to learn and know for themselves.

Kelly Ahuna, PhD, is the Director of the Office of Academic Integrity at the University at Buffalo (UB). Faculty for 20 years, Kelly first ran an undergraduate critical thinking program and later worked with graduate teacher candidates on curriculum and instruction. Her overarching interest in student and teacher success led her to the growing field of academic integrity work and her current role as the inaugural director at UB where she has developed policy and procedures, established a robust remediation process, and co-founded the ICAI Northeast Regional Consortium. Kelly holds a bachelor’s degree in English with secondary teacher certification from Dickinson College, a master’s degree in higher education administration from the University of Vermont, and a doctoral degree in sociology of education from UB.

Michael Kiener, PhD, CRC is a professor at Maryville University of St. Louis in their Clinical Mental Health Counseling program. For the past 10 years he has coordinated their Scholarship of Teaching and Learning Program, where faculty participate in a yearlong program with a goal of improved student learning. In 2012 and 2024 he received the Outstanding Faculty Award for faculty who best demonstrate excellence in the integration of teaching, scholarship and/or service. He has over thirty publications including a co-authored book on strength-based counseling and journal articles on career decision making, action research, counseling pedagogy, and active and dynamic learning strategies.

References

Petricini, T., Wu, C., & Zipf, S. (2024). Perceptions about artificial intelligence and chatGPT use by faculty and students. Transformative Dialogues: Teaching and Learning Journal, 17(2), 63-87. https://doi.org/10.26209/td2024vol17iss21825

Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9(5) Retrieved from http://www.marcprensky.com/writing/Prensky%20- %20Digital%20Natives,%20Digital%20Immigrants%20-%20Part1.pdf
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August 6, 2025
When ZIP Codes Teach: How Geographic Inequity Shows Up in Our Classrooms – Faculty Focus

What if I told you the education system isn’t broken—it’s working exactly the way it was built to? That’s not just a soundbite from my TEDx Talk—it’s my lived experience.

I grew up in a historically underserved neighborhood in Houston, Texas, zoned to one of the lowest-performing elementary schools in the city. But due to a granted academic transfer, I ended up attending a top-performing public school just three miles away. Same city. Different ZIP code. Completely different resources—and trajectory.

That three-mile difference changed everything. And today, as a doctoral student and aspiring professor, I can see how those early disparities still show up in our college classrooms.

Here’s the thing: public school funding in the U.S. depends heavily on local property taxes. That means wealthier neighborhoods get better schools—often with state-of-the-art facilities, robust extracurriculars, and college prep courses—while nearby communities may struggle with crumbling infrastructure and underpaid staff (Owens-Young 2023).

Part of my dissertation, I compared two elementary schools less than three miles apart. One had a PTA raising over $100,000 annually. The other didn’t have working air conditioning. These weren’t just different schools—they were different worlds. And students from those environments bring that context with them when they walk into our lecture halls.

But what we see in higher education—missed assignments, low engagement, quiet classrooms—isn’t always a motivation issue. Often, it’s a preparedness gap. And that gap isn’t the student’s fault.

As faculty, we can’t change where our students come from. But we can shape what they experience once they’re here. Below are four strategies I use in the classroom to help level the playing field:

1. Start by Asking, Not Assuming

Instead of assuming a lack of interest, ask about their background. I use a quick, anonymous prompt on the first day of class: “What’s one thing you overcame to get here today?” One student wrote about taking two buses from the other side of town after getting their younger siblings off to school. That kind of insight changes how we teach.

2. Rethink Participation

Not everyone feels comfortable speaking up in class. Consider using written reflections, discussion boards, or tools like Jamboard and Padlet. For instance, in one of my leadership classes, I use a shared Google Doc where students add thoughts anonymously. The quieter students thrive—and the louder ones gain new perspectives.

3. Normalize Struggle

Students from under-resourced schools often view academic struggle as failure. That’s why I share my own story of nearly failing out during undergrad. I also let students revise one major assignment. One student improved from a 72 to a 90—and more importantly, realized they were capable all along.

4. Offer Flexible Assessment Options

Some students express themselves better visually or verbally. I once had a student create a narrated photo essay instead of a traditional paper—it was powerful, reflective, and demonstrated full mastery. When students see that their strengths are valued, their confidence grows.

None of these ideas require overhauling your entire course. But they do require intentionality. And that’s what makes the difference.

I’ve been the under-resourced student, and now I’m becoming the professor I wish I had—someone who saw potential beyond performance.

We don’t control our students’ ZIP codes. But we do influence how they feel in our classrooms—and whether they believe they belong there.

Lloyd Lindley Jr. is a PhD student in Educational Leadership and Organization at Texas Woman’s University, a TEDx speaker, and author of “From the Hood to Understood”. He is the co-founder of a grassroots nonprofit that advocates for equitable access to education, mentorship, and financial literacy in underserved communities. Lloyd has been featured on Good Morning Houston and KTSU Radio for his impactful community work and continues to champion culturally responsive teaching and student empowerment at all levels of education.

References

Owens-Young, Jessica. 2023. “The ZIP Code Effect.” American University.

Source link

July 11, 2025
When ZIP Codes Teach: How Geographic Inequity Shows Up in Our Classrooms – Faculty Focus

What if I told you the education system isn’t broken—it’s working exactly the way it was built to? That’s not just a soundbite from my TEDx Talk—it’s my lived experience.

I grew up in a historically underserved neighborhood in Houston, Texas, zoned to one of the lowest-performing elementary schools in the city. But due to a granted academic transfer, I ended up attending a top-performing public school just three miles away. Same city. Different ZIP code. Completely different resources—and trajectory.

That three-mile difference changed everything. And today, as a doctoral student and aspiring professor, I can see how those early disparities still show up in our college classrooms.

Here’s the thing: public school funding in the U.S. depends heavily on local property taxes. That means wealthier neighborhoods get better schools—often with state-of-the-art facilities, robust extracurriculars, and college prep courses—while nearby communities may struggle with crumbling infrastructure and underpaid staff (Owens-Young 2023).

Part of my dissertation, I compared two elementary schools less than three miles apart. One had a PTA raising over $100,000 annually. The other didn’t have working air conditioning. These weren’t just different schools—they were different worlds. And students from those environments bring that context with them when they walk into our lecture halls.

But what we see in higher education—missed assignments, low engagement, quiet classrooms—isn’t always a motivation issue. Often, it’s a preparedness gap. And that gap isn’t the student’s fault.

As faculty, we can’t change where our students come from. But we can shape what they experience once they’re here. Below are four strategies I use in the classroom to help level the playing field:

1. Start by Asking, Not Assuming

Instead of assuming a lack of interest, ask about their background. I use a quick, anonymous prompt on the first day of class: “What’s one thing you overcame to get here today?” One student wrote about taking two buses from the other side of town after getting their younger siblings off to school. That kind of insight changes how we teach.

2. Rethink Participation

Not everyone feels comfortable speaking up in class. Consider using written reflections, discussion boards, or tools like Jamboard and Padlet. For instance, in one of my leadership classes, I use a shared Google Doc where students add thoughts anonymously. The quieter students thrive—and the louder ones gain new perspectives.

3. Normalize Struggle

Students from under-resourced schools often view academic struggle as failure. That’s why I share my own story of nearly failing out during undergrad. I also let students revise one major assignment. One student improved from a 72 to a 90—and more importantly, realized they were capable all along.

4. Offer Flexible Assessment Options

Some students express themselves better visually or verbally. I once had a student create a narrated photo essay instead of a traditional paper—it was powerful, reflective, and demonstrated full mastery. When students see that their strengths are valued, their confidence grows.

None of these ideas require overhauling your entire course. But they do require intentionality. And that’s what makes the difference.

I’ve been the under-resourced student, and now I’m becoming the professor I wish I had—someone who saw potential beyond performance.

We don’t control our students’ ZIP codes. But we do influence how they feel in our classrooms—and whether they believe they belong there.

Lloyd Lindley Jr. is a PhD student in Educational Leadership and Organization at Texas Woman’s University, a TEDx speaker, and author of “From the Hood to Understood”. He is the co-founder of a grassroots nonprofit that advocates for equitable access to education, mentorship, and financial literacy in underserved communities. Lloyd has been featured on Good Morning Houston and KTSU Radio for his impactful community work and continues to champion culturally responsive teaching and student empowerment at all levels of education.

References

Owens-Young, Jessica. 2023. “The ZIP Code Effect.” American University.

Source link

July 11, 2025

Category: student learning

Teaching, Learning, and Engagement Strategies

Assess Students’ Readiness for Research

Model Behavior and Coach for Success

Introduce and Teach Project Management Skills

Create a Safe Space for New Experiences and Growth

Why Students Turn to AI

Process Over Product

Disciplinary Innovation

Creative Disciplines

Simulating Complexity

Modeling Scientific Reasoning

Building AI Fluency Across the Curriculum

Why Students Turn to AI

Process Over Product

Disciplinary Innovation

Creative Disciplines

Simulating Complexity

Modeling Scientific Reasoning

Building AI Fluency Across the Curriculum

The Context

The Illustration

The Discussion

The Application

The OARS Framework

Rephrasing

Active Listening

The OARS Framework

Rephrasing

Active Listening

Instructors can:

1. Start by Asking, Not Assuming

2. Rethink Participation

3. Normalize Struggle

4. Offer Flexible Assessment Options

1. Start by Asking, Not Assuming

2. Rethink Participation

3. Normalize Struggle

4. Offer Flexible Assessment Options