Category: designing assignments

Designing AI-Resistant Assignments in Educational Leadership Courses – Faculty Focus
When I began teaching graduate-level courses in educational leadership early in the COVID-19 pandemic, my classroom existed entirely online. I quickly learned that keeping students engaged through a screen required more than well-organized slides or polished lectures. It required activities, learning experiences, and assignments that drew students into authentic, applied work that connected theory to the real leadership challenges they would face. Since then, I have taught dozens of educational leadership courses, both online and in person, and I have seen the same pattern: the design of an assignment shapes not only what students submit, but how deeply they engage with the learning process. Now, with generative AI able to produce essays, summaries, and even comprehensive education plans in seconds, the stakes for assignment design have changed. If an assignment can be completed entirely by an AI tool without any meaningful student input, we risk undermining the very skills that matter most in leadership preparation.

Why AI-Resistant Assignments Matter

Some educators have begun using the term “AI-resistant assignments” to describe work that cannot be fully completed by a chatbot. These are not AI-proof, and I am not trying to exclude technology altogether. In fact, when used appropriately, AI can be a valuable support for brainstorming, organizing ideas, or refining language. But an AI-resistant assignment is one where the core of the work, including the evidence gathered, the decisions made, and the reflections offered, must come from the student’s own engagement with people, data, and context.

Other educators have reached similar conclusions. In a recent New York Times column, Grose (2025) profiled professors who are reimagining their courses to ensure students remain active participants in their own learning. The examples she shared came from the humanities, but the underlying lesson applies across fields: in an AI-integrated world, we have to create assignments that cannot be outsourced entirely to a machine. The University of Massachusetts Amherst Center for Teaching and Learning (n.d.) offers strategies for making assignments more resistant to AI completion, such as requiring site-specific context, process documentation, and collaborative components.

Further underscoring the need for evidence, the AutomatED newsletter issued a public challenge to educators to submit assignments they believed were “AI-immune” and then tested them directly with AI tools (Clay, 2023). The results demonstrated that only by actively trialing assignments against AI, not just assuming immunity, can instructors be confident in their design choices.

These realities make it clear that designing assignments for leadership preparation cannot be left to chance. Educators need intentional strategies that not only account for AI’s capabilities but also reinforce the kinds of professional thinking and application AI cannot replicate. The following five principles offer a practical foundation for developing assignments that engage students deeply, foster leadership competencies, and ensure authentic performance.

Five Principles for AI-Resistant Assignments

From my own practice and from published guidance, I have developed five principles for AI-resistant assignments in leadership preparation:
1. Ground in authentic context – Require site-specific or scenario-specific evidence, incorporate the policy and community realities of schools, and engage peers or stakeholders in the process. This can include interactive formats such as presentations (recorded or live), simulations, debates, classroom observations with low-inference notes, analysis of instructional practice, interviews with stakeholders, or structured peer feedback sessions.
1. Connect theory to practice – Anchor assignments in leadership frameworks such as the Professional Standards for Educational Leaders, Lencioni’s Organizational Health, or Kotter’s change model, and always include both application and contextualized reflection.
1. Make the learning process visible – Assess drafts and planning notes alongside final products, and encourage multimodal evidence such as charts, agendas, or observation notes.
1. Clarify the role of AI – Set explicit expectations for when AI can be used and when it cannot replace original, site-based analysis.
1. Center human presence (Saucier’s PEACE Framework) – Build assignments that highlight preparation, expertise, authenticity, care, and engagement — qualities that cannot be automated and remain central to effective teaching (Saucier, 2025). I make sure that my instructions are clear, grounded in tasks they will use in the field, and meaningful for them to complete in their future leadership positions.
Examples from Leadership Preparation

These principles translate into assignments that blend conceptual rigor with applied relevance:
- Community Introduction Town Hall – District leader candidates prepare and deliver a live presentation introducing themselves to the school community during an initial town hall meeting. The presentation includes school demographics, achievement data, community assets, and identified needs, and is followed by a brief Q&A. This assignment is explicitly aligned to the Professional Standards for Educational Leaders (PSEL) and requires candidates to connect their leadership vision to stakeholder priorities.
- Organizational Health Rubric and Evaluation – Students use research to build on Patrick Lencioni’s organizational health model to create a custom rubric tailored to the context of a school district. They then apply the rubric to conduct an assessment, gathering both qualitative and quantitative evidence, rating each dimension, and concluding with a prioritized set of recommendations for improvement.
- Clinical Internship Logs with Contextual Analysis and Showcase – Students maintain weekly logs documenting activities, decisions, and reflections from their clinical internship, ensuring each entry explicitly connects to course content and relevant standards such as the Professional Standards for Educational Leaders (PSEL). At the end of the course, they present a synthesis of their experiences in a live or recorded showcase, highlighting key takeaways, challenges addressed, and leadership growth.
- Practice Instructional Coaching Conversations – Prepare for and conduct a simulated coaching conversation using an observation or case scenario, applying a framework like Jim Knight’s Impact Cycle, followed by reflection on the exchange.
Implementation Strategies

When implementing these assignments, I sequence them so that simpler applications build into more complex, multi-stakeholder tasks, and I scaffold the skills of data collection, context analysis, and reflective practice. I apply UMass CTL’s transparency recommendations, making the purpose of each assignment explicit, clarifying AI use policies, and linking tasks directly to learning outcomes. I also keep the PEACE framework in mind in both my teaching and my assessment design.

Conclusion: Keeping Humans at the Center

AI-resistant assignment design not only maintains academic integrity; it prepares leadership candidates for the realities of their profession. They practice making decisions with incomplete information, balancing competing interests, and communicating effectively with stakeholders, all in contexts where AI can be a partner but never the whole answer. By aligning these assignments with standards such as the Professional Standards for Educational Leaders and the National Educational Leadership Preparation standards, we ensure graduates leave with the judgment, adaptability, and human skills their roles demand. In an age when AI can produce passable work in seconds, including comprehensive education plans, the temptation for both students and instructors is to let the machine take the lead. But leadership is not about producing text; it is about guiding people and systems toward a shared vision. As Grose (2025) observed, the most enduring learning happens when students must engage with real people and real problems. If we want to prepare leaders who can thrive, we must ensure that every leadership course includes at least one AI-resistant assignment, not as a constraint, but as a commitment to developing the human capacities that no algorithm can replace.

Andy Szeto, EdD, is an adjunct professor in educational leadership with experience teaching more than fifty graduate-level courses. He specializes in instructional leadership, school management, and AI integration in school leadership preparation, drawing from his professional background as a district leader and his ongoing work mentoring emerging school leaders.

References

Clay, G. (2023, April 3). Believe your assignment is AI-immune? Let’s put it to the test. AutomatED: Teaching Better with Tech. https://automatedteach.com/p/believe-assignment-aiimmune-lets-put-test

Grose, J. (2025, August 6). These college professors will not bow down to A.I. The New York Times. https://www.nytimes.com/2025/08/06/opinion/humanities-college-ai.html

Saucier, L. (2025, May 12). What can college instructors offer their students in the age of AI? Faculty Focus. https://www.facultyfocus.com/articles/teaching-with-technology-articles/what-can-college-instructors-offer-their-students-in-the-age-of-ai/

University of Massachusetts Amherst Center for Teaching and Learning. (n.d.). How do I redesign assignments and assessments for an AI-impacted world? Retrieved August 9, 2025, from https://www.umass.edu/ctl/how-do-i-redesign-assignments-and-assessments-ai-impacted-world
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January 12, 2026

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

The Art of Collaboration: Designing Assignments That Work – Faculty Focus
Inclusivity and feelings of psychological safety in the classroom should not be reserved solely for K-12 learning environments. Students in higher education also deserve nurturing spaces that focus on utilizing their personalized strengths and needs to foster increased academic and social development. A costly misconception is that pedagogical approaches in higher education must be lecture-based and teacher-led. Instead, it is an educator’s responsibility to design cooperative learning structures that facilitate comfort and collaboration regardless of grade level. These practical techniques will develop critical thinking and problem-solving abilities by creating intentional opportunities to enhance social and communication skills. The fundamental question becomes: How can educators plan for and deliver instructional content that is relatable and meaningful?

According to a recent survey by Salesforce (2022), just 12% of college students felt a strong sense of belonging in their institutions. Feelings of disconnection from their peers and institutions persisted after students returned to campus from remote learning during the COVID-19 pandemic. To address this dismal response to education, we must promote inclusive and respectful learning environments that foster engagement, motivation, and active discourse.

Purposeful Application to Teamwork

A practical and successful strategy is to design performance tasks using cooperative learning structures deliberately. Aside from promoting positive interdependence and individual accountability, students participate equally through simultaneous interactions. These actionable, collaborative strategies help educators navigate their role as facilitators, guiding and responding rather than lecturing and directing.

In an undergraduate education course, I establish a positive tone by holding morning meetings at the start of every class. The intention is to incorporate short, structured routines that foster community, address social-emotional needs, and transition students to the day’s lesson. Purposefully, I provide opportunities for students to connect and practice the social skills necessary to participate in rigorous critical thinking and problem-solving tasks. The goal is to elicit positive peer interactions and foster a sense of belonging and trust where students consider multiple perspectives and actively engage in an inclusive and receptive learning environment.

To enrich their classroom experience, I align cooperative learning structures with the lesson outcomes, ensuring that 21st-century, real-world content tasks are purposeful and authentic. Motivated by intentional opportunities for collaboration, students actively process information in favorable conditions that are both supportive and stimulating.

Unlike group work, cooperative learning activities are highly structured, with defined roles, steps, and time limits. With transparent directions, every student knows exactly what to do. Another key difference is that these structures promote built-in accountability and equal participation, requiring everyone to contribute through regular interaction and processing.

When considering a teacher’s pedagogical content knowledge, eliciting student feedback is paramount. Responding to end of course surveys, student reflections highlight the importance of the affective domain through several emerging pivotal themes. Students emphasized the significance of engaging, inclusive, and supportive learning environments. This aligns with findings from the literature, which posits that allowing students to apply course content in innovative and engaging ways fosters a deeper understanding and a heightened sense of ownership in learning (Singha & Singha, 2024). Given these findings, creating a classroom community through collaboration increases motivation and encourages students to develop their critical thinking skills and analyze problems more effectively.

Some students conveyed appreciation for interactive, relational, and consistently structured learning experiences. One student communicated, “I felt heard, valued, and important in this class… everyone truly has the opportunity to openly share without judgment.” These emotional and interpersonal experiences support long-term learning and boost goal-directed behaviors.

Another key theme was the acknowledgment of active, collaborative, and applied learning activities. One student articulated the need for relatable, real-world, authentic tasks, stating, “My favorite activity was creating videos to connect with the assignments in class.” This reflection highlights the notion that students in higher education value opportunities that enable them to transition from passive consumers of information to active participants in the learning process (Ribeiro-Silva et al., 2022). Moreover, recent research supports the implementation of faculty professional development programs focused on active learning instruction and engaging students in college classrooms (Park & Xu, 2024).

Implications for Faculty

To design assignments that work and adopt a reflective framework in your own instruction, consider implementing the following practices:
1. Set Clear Learning Objectives: Ensure students understand what they are expected to accomplish during collaboration. Cooperative learning is most effective when aligned with specific outcomes, especially those that require higher-order thinking (analysis, synthesis, and application). Identify what students should know, do, or be able to explain by the end of the activity.
1. Design Purposeful and Flexible Groups: Keep it interesting and diversify student groups. Avoid simple tasks that can be done individually—design work that fosters interdependence and leverages diverse perspectives. Create tasks that are complex enough to require collaboration (e.g., solving a case study, evaluating evidence, or designing a project).
1. Structure Group Roles and Expectations: Assign roles based on instructional content to ensure equal participation and engagement among group members. Structured roles promote engagement and accountability. Rotate roles to ensure individual accountability and contribution.
1. Monitor and Support Groups in Real Time: Circulate during group work, check for understanding, and intervene as needed to guide discussion or clarify misconceptions. Guide communication, active listening, respectful disagreement, and group decision-making. Active monitoring shows students their work matters and allows faculty to model and scaffold skills often.
1. Encourage Metacognition: Provide opportunities for students to reflect on their group work experiences and the processes involved. Reflection reinforces academic content and collaboration skills.
By implementing collaborative techniques, faculty can motivate students to apply content knowledge that will prepare their future success in relatable, real-world contexts and encourage the development and maintenance of the interpersonal skills necessary for lifelong learning. Furthermore, by refining our pedagogical practices, we can foster a more robust society with confident, culturally competent, and self-aware leaders of tomorrow.

Final Reflections

Former U.S. Secretary of Education Miguel Cardona quoted the African proverb, “If you want to go fast, go alone. If you want to go far, go together.” University settings often overlook effective pedagogical approaches that incorporate collaboration, limiting opportunities for meaningful learning experiences that enhance understanding, sustain motivation, and spark intellectual curiosity. By designing, facilitating, monitoring, and evaluating inclusive learning spaces, we can contribute to a sense of belonging and bring joy back into the classroom.

Dr. Ana Figueroa is an assistant professor of education at the University of Tampa and the Lead Instructor of the Education Foundations and Human Exceptionalities courses in the undergraduate program. Her research interests include differentiated instruction, progress monitoring, teacher mindset, and highly effective instructional strategies. She champions equitable instruction for all learners.

References

Kagan, Spencer, and Miguel Kagan. Kagan Cooperative Learning. San Clemente, CA: Kagan Publishing, 2021.

Namahoe, K. (2022, April 15). Secretary Cardona’s 6 Pathways Forward in Education. ascd.org. December 29, 2024, https://ascd.org/blogs/secretary-cardonas-6-pathways-forward-in-education

Park, Elizabeth S., and Di Xu. “The Effect of Active Learning Professional Development Training on College Students’ Academic Outcomes.” Journal of Research on Educational Effectiveness 17, no. 1 (December 20, 2024): 43–64. https://doi.org/10.1080/19345747.2022.2151954.

Ribeiro-Silva, Elsa, Catarina Amorim, José Luis Aparicio-Herguedas, and Paula Batista. “Trends of Active Learning in Higher Education and Students’ Well-Being: A Literature Review.” Frontiers in Psychology 13 (April 18, 2022). https://doi.org/10.3389/fpsyg.2022.844236.

“Salesforce,” October 6, 2022. https://www.salesforce.com/news/stories/higher-education-statistics-trends-2022/.

Singha, Ranjit, and Surjit Singha. “Educational Innovation Transforming Higher Education for Workforce Readiness.” Advances in Higher Education and Professional Development, January 29, 2024, 37–55. https://doi.org/10.4018/979-8-3693-0517-1.ch003.
Source link
July 16, 2025
The Art of Collaboration: Designing Assignments That Work – Faculty Focus
Inclusivity and feelings of psychological safety in the classroom should not be reserved solely for K-12 learning environments. Students in higher education also deserve nurturing spaces that focus on utilizing their personalized strengths and needs to foster increased academic and social development. A costly misconception is that pedagogical approaches in higher education must be lecture-based and teacher-led. Instead, it is an educator’s responsibility to design cooperative learning structures that facilitate comfort and collaboration regardless of grade level. These practical techniques will develop critical thinking and problem-solving abilities by creating intentional opportunities to enhance social and communication skills. The fundamental question becomes: How can educators plan for and deliver instructional content that is relatable and meaningful?

According to a recent survey by Salesforce (2022), just 12% of college students felt a strong sense of belonging in their institutions. Feelings of disconnection from their peers and institutions persisted after students returned to campus from remote learning during the COVID-19 pandemic. To address this dismal response to education, we must promote inclusive and respectful learning environments that foster engagement, motivation, and active discourse.

Purposeful Application to Teamwork

A practical and successful strategy is to design performance tasks using cooperative learning structures deliberately. Aside from promoting positive interdependence and individual accountability, students participate equally through simultaneous interactions. These actionable, collaborative strategies help educators navigate their role as facilitators, guiding and responding rather than lecturing and directing.

In an undergraduate education course, I establish a positive tone by holding morning meetings at the start of every class. The intention is to incorporate short, structured routines that foster community, address social-emotional needs, and transition students to the day’s lesson. Purposefully, I provide opportunities for students to connect and practice the social skills necessary to participate in rigorous critical thinking and problem-solving tasks. The goal is to elicit positive peer interactions and foster a sense of belonging and trust where students consider multiple perspectives and actively engage in an inclusive and receptive learning environment.

To enrich their classroom experience, I align cooperative learning structures with the lesson outcomes, ensuring that 21st-century, real-world content tasks are purposeful and authentic. Motivated by intentional opportunities for collaboration, students actively process information in favorable conditions that are both supportive and stimulating.

Unlike group work, cooperative learning activities are highly structured, with defined roles, steps, and time limits. With transparent directions, every student knows exactly what to do. Another key difference is that these structures promote built-in accountability and equal participation, requiring everyone to contribute through regular interaction and processing.

When considering a teacher’s pedagogical content knowledge, eliciting student feedback is paramount. Responding to end of course surveys, student reflections highlight the importance of the affective domain through several emerging pivotal themes. Students emphasized the significance of engaging, inclusive, and supportive learning environments. This aligns with findings from the literature, which posits that allowing students to apply course content in innovative and engaging ways fosters a deeper understanding and a heightened sense of ownership in learning (Singha & Singha, 2024). Given these findings, creating a classroom community through collaboration increases motivation and encourages students to develop their critical thinking skills and analyze problems more effectively.

Some students conveyed appreciation for interactive, relational, and consistently structured learning experiences. One student communicated, “I felt heard, valued, and important in this class… everyone truly has the opportunity to openly share without judgment.” These emotional and interpersonal experiences support long-term learning and boost goal-directed behaviors.

Another key theme was the acknowledgment of active, collaborative, and applied learning activities. One student articulated the need for relatable, real-world, authentic tasks, stating, “My favorite activity was creating videos to connect with the assignments in class.” This reflection highlights the notion that students in higher education value opportunities that enable them to transition from passive consumers of information to active participants in the learning process (Ribeiro-Silva et al., 2022). Moreover, recent research supports the implementation of faculty professional development programs focused on active learning instruction and engaging students in college classrooms (Park & Xu, 2024).

Implications for Faculty

To design assignments that work and adopt a reflective framework in your own instruction, consider implementing the following practices:
1. Set Clear Learning Objectives: Ensure students understand what they are expected to accomplish during collaboration. Cooperative learning is most effective when aligned with specific outcomes, especially those that require higher-order thinking (analysis, synthesis, and application). Identify what students should know, do, or be able to explain by the end of the activity.
1. Design Purposeful and Flexible Groups: Keep it interesting and diversify student groups. Avoid simple tasks that can be done individually—design work that fosters interdependence and leverages diverse perspectives. Create tasks that are complex enough to require collaboration (e.g., solving a case study, evaluating evidence, or designing a project).
1. Structure Group Roles and Expectations: Assign roles based on instructional content to ensure equal participation and engagement among group members. Structured roles promote engagement and accountability. Rotate roles to ensure individual accountability and contribution.
1. Monitor and Support Groups in Real Time: Circulate during group work, check for understanding, and intervene as needed to guide discussion or clarify misconceptions. Guide communication, active listening, respectful disagreement, and group decision-making. Active monitoring shows students their work matters and allows faculty to model and scaffold skills often.
1. Encourage Metacognition: Provide opportunities for students to reflect on their group work experiences and the processes involved. Reflection reinforces academic content and collaboration skills.
By implementing collaborative techniques, faculty can motivate students to apply content knowledge that will prepare their future success in relatable, real-world contexts and encourage the development and maintenance of the interpersonal skills necessary for lifelong learning. Furthermore, by refining our pedagogical practices, we can foster a more robust society with confident, culturally competent, and self-aware leaders of tomorrow.

Final Reflections

Former U.S. Secretary of Education Miguel Cardona quoted the African proverb, “If you want to go fast, go alone. If you want to go far, go together.” University settings often overlook effective pedagogical approaches that incorporate collaboration, limiting opportunities for meaningful learning experiences that enhance understanding, sustain motivation, and spark intellectual curiosity. By designing, facilitating, monitoring, and evaluating inclusive learning spaces, we can contribute to a sense of belonging and bring joy back into the classroom.

Dr. Ana Figueroa is an assistant professor of education at the University of Tampa and the Lead Instructor of the Education Foundations and Human Exceptionalities courses in the undergraduate program. Her research interests include differentiated instruction, progress monitoring, teacher mindset, and highly effective instructional strategies. She champions equitable instruction for all learners.

References

Kagan, Spencer, and Miguel Kagan. Kagan Cooperative Learning. San Clemente, CA: Kagan Publishing, 2021.

Namahoe, K. (2022, April 15). Secretary Cardona’s 6 Pathways Forward in Education. ascd.org. December 29, 2024, https://ascd.org/blogs/secretary-cardonas-6-pathways-forward-in-education

Park, Elizabeth S., and Di Xu. “The Effect of Active Learning Professional Development Training on College Students’ Academic Outcomes.” Journal of Research on Educational Effectiveness 17, no. 1 (December 20, 2024): 43–64. https://doi.org/10.1080/19345747.2022.2151954.

Ribeiro-Silva, Elsa, Catarina Amorim, José Luis Aparicio-Herguedas, and Paula Batista. “Trends of Active Learning in Higher Education and Students’ Well-Being: A Literature Review.” Frontiers in Psychology 13 (April 18, 2022). https://doi.org/10.3389/fpsyg.2022.844236.

“Salesforce,” October 6, 2022. https://www.salesforce.com/news/stories/higher-education-statistics-trends-2022/.

Singha, Ranjit, and Surjit Singha. “Educational Innovation Transforming Higher Education for Workforce Readiness.” Advances in Higher Education and Professional Development, January 29, 2024, 37–55. https://doi.org/10.4018/979-8-3693-0517-1.ch003.
Source link
July 16, 2025
5 Steps to Update Assignments to Foster Critical Thinking and Authentic Learning in an AI Age – Faculty Focus

I recently attended a student panel on use of AI in college classes. The three panelists shared their perspectives, borne out in an April 2025 Gallup survey: they want to learn to use AI tools in appropriate ways, they’re anxious about the potential negative impact on their critical thinking skills, and they want explicit guidance from their faculty members—which they’re not consistently getting.

Even, or especially, in an AI age, we know our students need to think critically and learn authentically to be productive and engaged citizens and future employees. We recognize we need to update assignments to promote academic integrity in an AI age, yet many of us feel unprepared to do so. The crux of the problem is this: how do we foster critical thinking and authentic learning when it’s so easy for students to outsource their cognitive work?

One promising solution to the triple challenge of fostering critical thinking, meaningful learning, and academic integrity is to double down on transparency. We can provide the guidance students want, embed analysis and evaluation into our assignments to get at that all-important critical thinking, and nudge students toward integrity. How? By embracing transparency.

Let’s detail our expectations for responsible AI use. Let’s emphasize the why: why we want students to use their own cognitive abilities for some tasks, why using AI could be helpful at times, and why we’ve crafted our AI-integrated assignments in the ways we have. Finally, let’s ask students to disclose their use in full transparency (and reflect on their learning, as Marc Watkins argues here).

In short, we can promote integrity when assessments are meaningful, purposeful, and include transparent instruction on appropriate AI use. We can teach students to critically analyze AI-assisted work and promote authentic learning as we do. To help us achieve these aims, here are five steps to update assignments in ways that help students learn the skills they need while fostering a culture of academic honesty.

Step 1: Take a critical look at your current syllabus.

If you’re like me, you have tried-and-true activities and assignments that you’ve relied on for many years. Some of these may be updateable, but others may just need to go. If AI can easily complete a task (try running your instructions through a tool like ChatGPT to find out), maybe it’s no longer a relevant measure of authentic learning. Also, following the remaining steps below will likely mean that you add new instructional practices (like modelling AI use) and new components of the assignments you update (like a reflection on how AI helped or hindered learning). Make time for these important new course elements by getting rid of outdated tasks.

Step 2: Consider whether and how students should use AI on the assignments you keep.

Remember, students want to know exactly what is appropriate for AI use in your class. A helpful tool for this process is the 5-level AI Assessment Scale (AIAS). The levels range from No AI, AI Planning, AI Collaboration, Full AI, and AI Exploration. Each one identifies and sanctions different ways students can use AI in appropriate and meaningful ways to support their learning.

Leon Furze, one of the creators of the AIAS, presents it a tool that helps update our assignments and start conversations with our students. He recommends using this framework to guide your thinking: break down each assignment, project, or discussion board activity into disparate steps, then decide for which parts of the process AI use is appropriate. Providing transparent guidance, guidance that may vary for different parts of the project, is what students need right now.

Step 3: Carve out time to discuss and model your expectations.

Many students are not sure what is acceptable in this current moment. What better way to help them feel confident while developing the AI skills they need than modelling what you’re looking for? Take class time or record a video for your online class to teach your students what you expect them to do with AI for each assignment, what not to do, and what you’ll be looking for in their finished product.

As you approach this aspect of instruction, take heart. I don’t think every instructor needs to be an AI expert, but I do think we’re doing our students a disservice if we’re not teaching them how to be AI Whisperers, a concept I gleaned from Ethan Mollick’s 2024 book, Co-Intelligence. A new kind of expertise is emerging, he argues: the skilled and effective use of AI in every field. As disciplinary experts, you know what kind of AI use will help students learn your material and what will disempower them as learners. Let’s allocate time to help students develop AI literacy while maintaining their agency.

Step 4: Ask students to disclose their AI use, as noted above.

The student panelists showed palpable relief when they described instructors who simply had them cite their use of AI. “Encourage your students to be transparent about their AI use,” one student, a finance major, said. As she talked about how simple it was to add a line at the end of the assignment such as, “ChatGPT generated the outline for this essay,” the anxiety that had previously clouded her expression disappeared.

One approach is to use the AI Disclosure (AID) framework to document how students used AI, or add an appendix to each assignment, or add comments or footnotes to make transparent what they wrote and what AI wrote. Get students to declare their AI use and even better, ask them to reflect on whether their use helped or hindered their learning.

Step 5: If you suspect inappropriate use of AI, don’t accuse students of cheating.

Instead, have a conversation with them. Recall that a primary goal of the AIAS is to facilitate discussions about AI use. Imagine that as a class, you agreed that Level 2 (AI Planning) was appropriate for a particular project. If a student submits work that seems to have been completed at Level 4 (Full AI), refer back to the framework to facilitate a conversation about your perception of their work and their process. As AIAS co-creator Leon Furze points out, now you can get away from wondering whether a student cheated with AI and instead ask, “can I make a valid judgement of this student’s capabilities [whether they have used AI or not]?”

And as argued by the authors of a new book titled The Opposite of Cheating: Teaching for Integrity in an Age of AI, this takes moral accusation out of the conversation, reduces the adversarial tone, and enables students to reflect on and hopefully learn from the experience. You can also decide together what actions should take place next, whether the student resubmits the assignment in accordance with your AI-usage guidance, whether they lose the opportunity to earn a grade for that assignment, or whatever else you and your student decide is appropriate in that situation.

Students want to know how to use AI well. “If we don’t teach students how to use AI responsibly, they’re going to be poorly equipped for the workforce,” said the third student panelist, a computer science major. Let’s update our assignments to facilitate meaningful learning of our content, the skills students need in an AI age, and ethical, integrous use of AI to equip them for the workplace they’ll enter. In doing so, we’re helping to develop engaged citizens who think critically about information they encounter. I can’t think of more important work to be engaged in at this moment in time.

Flower Darby is an associate director of the Teaching for Learning Center at the University of Missouri, a co-author of The Norton Guide to Equity-Minded Teaching (2023), and the lead author, with James M. Lang, of Small Teaching Online: Applying Learning Science in Online Classes (2019).

References

Bertram Gallant, Tricia, and David A. Rettinger. 2025. The Opposite of Cheating: Teaching for Integrity in the Age of AI. University of Oklahoma Press.

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

Furze, Leon. 2025. LinkedIn post. https://www.linkedin.com/posts/leonfurze_artificialintelligence-aieducation-assessment-activity-7295247135480889346-UWFC/

Kassorla, M. 2024. “Our New Course: Some Highlights.” January 4. https://michellekassorla.substack.com/p/our-new-course-some-highlights

McMurtrie, Beth. 2025. “AI Unready.” January 30. https://www.chronicle.com/newsletter/teaching/2025-01-30?utm_source=Iterable&utm_medium=email&utm_campaign=campaign_12441556_nl_Teaching_date_20250130&sra=true

Mollick, E. 2024. Co-Intelligence: Living and Working with AI. Portfolio.

Walton Family Foundation and Gallup. 2025. How American Youth View and Use Artificial Intelligence. https://www.gallup.com/analytics/658901/walton-family-foundation-gallup-voices-gen-american-youth.aspx

Watkins, Marc. 2024. “Make AI Part of the Assignment.” October 2. https://www.chronicle.com/article/make-ai-part-of-the-assignment?sra=true

Weaver, Kari D. 2024. “The Artificial Intelligence Disclosure (AID) Framework: An Introduction.” College & Research Libraries News 85 (10). https://crln.acrl.org/index.php/crlnews/article/view/26548/34482

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July 9, 2025
Supporting the Instructional Design Process: Stress-Testing Assignments with AI – Faculty Focus

While many of our conversations have focused on what generative AI means for student assignments and learning outcomes, there’s another question faculty are asking—often individually and quietly: How can we leverage AI in our own academic and administrative work? And more importantly, should we?

The answer, I believe, lies in using AI to help clear space for the work only we can do—the collaboration, connection, and critical guidance that makes education transformative.

That doesn’t mean that we simply use AI as a crutch for answering emails or summarizing meetings. In fact, I believe the true promise of AI comes from using it, in Ethan Mollick’s words, as a “genuine intellectual partner,” one that can enhance classroom discussions, assist with creating engaging instructional materials, and even help develop sophisticated problem sets or simulations that previously required extensive preparation time. As Mollick says, “the focus needs to move from task automation to capability augmentation.”

AI offers many potential applications for faculty work. While faculty should continue to prioritize the importance of maintaining human connection, empathy, and support in our teaching practice, we need to consider other ways AI can augment our work. Perhaps one way is in the design of our courses, the assignments and activities that chart student progress across content and outcomes. But rather than asking AI to develop prompts or notes for us, we can use AI as a tool to help develop our work in surprising ways.

Works in Theory, Wobbles in Practice

We’ve all fallen in love with that one key discussion question or written assignment prompt that just fizzles in the classroom. Despite our best intentions, we may not provide enough information, or we fail to anticipate a blind spot that leads students down fruitless paths. One of the challenges of course design is that all our work can seem perfectly clear and effective when we are knee-deep in the design process, but everything somehow falls apart when deployed in the wild. From simple misunderstandings to complex misconceptions, these issues typically don’t reveal themselves until we see actual student work—often when it’s too late to prevent frustration.

Bridging this gap requires iterative refinement—recognizing that what works in theory or in controlled conditions needs real-world testing, adaptation, and continuous improvement. It’s not just about designing something that works in the lab but ensuring our designs are resilient, adaptable, and responsive enough to thrive in the wild.

While there’s no substitute for real-world testing, I began wondering if AI could help with this iterative refinement. I didn’t want AI to refine or tweak my prompts. I wanted to see if I could task AI with modelling hundreds of student responses to my prompts in the hope that this process might yield the kind of insight I was too close to see.

The Process: AI-Assisted Assignment Stress Testing

After experimenting with systems like Claude and ChatGPT, I’ve discovered they can effectively analyze and refine writing prompts through the creation of simulated student responses. The basic approach works like this. First, provide the AI with information about your course and key characteristics of your student population. Then, share the assignment prompt. The AI internally generates multiple simulated student responses across different skill levels. After, it provides a comprehensive analysis identifying potential issues and opportunities.

You might specify that the analysis include common misinterpretations students might make or any structural or organizational challenges in the prompt. But the AI can also identify content development patterns and potential issues as well as population-specific concerns based on your student demographics. Finally, the AI can even suggest refinements to the prompt.

Seeing What You’re Not Seeing

To test this approach, I uploaded a personal narrative prompt that asks students to connect their life experiences to their academic goals—a common assignment in first-year writing courses.

The AI analysis revealed several blind spots in my prompt design. For instance, I hadn’t considered how non-traditional students might struggle with “choice of major” language, since many are career-changers. The AI modeled responses also revealed that students might have difficulty transitioning between personal narrative and academic analysis sections. Most valuable was seeing how different student populations might interpret the same instructions. Career-changers might focus too heavily on work experiences, while others might struggle with how much personal information to share. These insights allowed me to add clarifying language and support materials before any real students encountered these challenges.

The entire process took about 30 minutes but potentially saved hours of student confusion and faculty clarification emails. Of course, AI responses aren’t identical to human student responses, and we should be cautious about viewing AI as an infallible expert or source of absolute truth. But used as an additional lens when developing assignments, this approach can grant course designers a different perspective, one that triggers valuable insights and potentially reduces workload.

If you’d like to try this approach yourself, here’s a template prompt you can use with AI systems.

Course Design Multiplier

This process allowed me to develop targeted support materials for predicted problem areas before students struggle, building proactive scaffolding into course design from the beginning. And by sharing insights gained through AI analysis, departments could collectively improve assignment design practices—particularly valuable for multi-section courses where consistency matters. Over time, we could build a practical library of “what works” that faculty could draw from, including analyses explaining why certain assignments succeed with particular student populations and learning objectives.

AI-assisted assignment analysis offers a promising tool that respects our expertise while expanding our ability to anticipate student needs. While the technology isn’t perfect and will never replace insights gained from direct student interaction, it provides a valuable perspective that helps identify blind spots before students encounter them. This represents just one way thoughtfully implemented AI can help us do more of what matters: creating meaningful learning experiences. By using AI for the predictive work of assignment design, we free more time and energy for the deeply human work of guiding and connecting with our students—the work that only we can do.

Dr. Nathan Pritts is a leader in higher education, specializing in faculty development, instructional innovation, and the integration of emerging technologies in teaching and learning. As Professor and Program Chair for First Year Writing at the University of Arizona Global Campus, he has spearheaded initiatives in strategic implementation of online learning technologies, comprehensive faculty training programs, and the creation of scalable interventions to support both faculty and students in online environments. As author and researcher, Dr. Pritts has published widely on topics including digital pedagogy, AI-enhanced curriculum design, assessment strategies, and the future of higher education.

Source link

March 19, 2025
Supporting the Instructional Design Process: Stress-Testing Assignments with AI – Faculty Focus

While many of our conversations have focused on what generative AI means for student assignments and learning outcomes, there’s another question faculty are asking—often individually and quietly: How can we leverage AI in our own academic and administrative work? And more importantly, should we?

The answer, I believe, lies in using AI to help clear space for the work only we can do—the collaboration, connection, and critical guidance that makes education transformative.

That doesn’t mean that we simply use AI as a crutch for answering emails or summarizing meetings. In fact, I believe the true promise of AI comes from using it, in Ethan Mollick’s words, as a “genuine intellectual partner,” one that can enhance classroom discussions, assist with creating engaging instructional materials, and even help develop sophisticated problem sets or simulations that previously required extensive preparation time. As Mollick says, “the focus needs to move from task automation to capability augmentation.”

AI offers many potential applications for faculty work. While faculty should continue to prioritize the importance of maintaining human connection, empathy, and support in our teaching practice, we need to consider other ways AI can augment our work. Perhaps one way is in the design of our courses, the assignments and activities that chart student progress across content and outcomes. But rather than asking AI to develop prompts or notes for us, we can use AI as a tool to help develop our work in surprising ways.

Works in Theory, Wobbles in Practice

We’ve all fallen in love with that one key discussion question or written assignment prompt that just fizzles in the classroom. Despite our best intentions, we may not provide enough information, or we fail to anticipate a blind spot that leads students down fruitless paths. One of the challenges of course design is that all our work can seem perfectly clear and effective when we are knee-deep in the design process, but everything somehow falls apart when deployed in the wild. From simple misunderstandings to complex misconceptions, these issues typically don’t reveal themselves until we see actual student work—often when it’s too late to prevent frustration.

Bridging this gap requires iterative refinement—recognizing that what works in theory or in controlled conditions needs real-world testing, adaptation, and continuous improvement. It’s not just about designing something that works in the lab but ensuring our designs are resilient, adaptable, and responsive enough to thrive in the wild.

While there’s no substitute for real-world testing, I began wondering if AI could help with this iterative refinement. I didn’t want AI to refine or tweak my prompts. I wanted to see if I could task AI with modelling hundreds of student responses to my prompts in the hope that this process might yield the kind of insight I was too close to see.

The Process: AI-Assisted Assignment Stress Testing

After experimenting with systems like Claude and ChatGPT, I’ve discovered they can effectively analyze and refine writing prompts through the creation of simulated student responses. The basic approach works like this. First, provide the AI with information about your course and key characteristics of your student population. Then, share the assignment prompt. The AI internally generates multiple simulated student responses across different skill levels. After, it provides a comprehensive analysis identifying potential issues and opportunities.

You might specify that the analysis include common misinterpretations students might make or any structural or organizational challenges in the prompt. But the AI can also identify content development patterns and potential issues as well as population-specific concerns based on your student demographics. Finally, the AI can even suggest refinements to the prompt.

Seeing What You’re Not Seeing

To test this approach, I uploaded a personal narrative prompt that asks students to connect their life experiences to their academic goals—a common assignment in first-year writing courses.

The AI analysis revealed several blind spots in my prompt design. For instance, I hadn’t considered how non-traditional students might struggle with “choice of major” language, since many are career-changers. The AI modeled responses also revealed that students might have difficulty transitioning between personal narrative and academic analysis sections. Most valuable was seeing how different student populations might interpret the same instructions. Career-changers might focus too heavily on work experiences, while others might struggle with how much personal information to share. These insights allowed me to add clarifying language and support materials before any real students encountered these challenges.

The entire process took about 30 minutes but potentially saved hours of student confusion and faculty clarification emails. Of course, AI responses aren’t identical to human student responses, and we should be cautious about viewing AI as an infallible expert or source of absolute truth. But used as an additional lens when developing assignments, this approach can grant course designers a different perspective, one that triggers valuable insights and potentially reduces workload.

If you’d like to try this approach yourself, here’s a template prompt you can use with AI systems.

Course Design Multiplier

This process allowed me to develop targeted support materials for predicted problem areas before students struggle, building proactive scaffolding into course design from the beginning. And by sharing insights gained through AI analysis, departments could collectively improve assignment design practices—particularly valuable for multi-section courses where consistency matters. Over time, we could build a practical library of “what works” that faculty could draw from, including analyses explaining why certain assignments succeed with particular student populations and learning objectives.

AI-assisted assignment analysis offers a promising tool that respects our expertise while expanding our ability to anticipate student needs. While the technology isn’t perfect and will never replace insights gained from direct student interaction, it provides a valuable perspective that helps identify blind spots before students encounter them. This represents just one way thoughtfully implemented AI can help us do more of what matters: creating meaningful learning experiences. By using AI for the predictive work of assignment design, we free more time and energy for the deeply human work of guiding and connecting with our students—the work that only we can do.

Dr. Nathan Pritts is a leader in higher education, specializing in faculty development, instructional innovation, and the integration of emerging technologies in teaching and learning. As Professor and Program Chair for First Year Writing at the University of Arizona Global Campus, he has spearheaded initiatives in strategic implementation of online learning technologies, comprehensive faculty training programs, and the creation of scalable interventions to support both faculty and students in online environments. As author and researcher, Dr. Pritts has published widely on topics including digital pedagogy, AI-enhanced curriculum design, assessment strategies, and the future of higher education.

Source link

March 19, 2025

Category: designing assignments

Why AI-Resistant Assignments Matter

Five Principles for AI-Resistant Assignments

Examples from Leadership Preparation

Implementation Strategies

Conclusion: Keeping Humans at the Center

Purposeful Application to Teamwork

Implications for Faculty

Final Reflections

Purposeful Application to Teamwork

Implications for Faculty

Final Reflections

Step 1: Take a critical look at your current syllabus.

Step 2: Consider whether and how students should use AI on the assignments you keep.

Step 3: Carve out time to discuss and model your expectations.

Step 4: Ask students to disclose their AI use, as noted above.

Step 5: If you suspect inappropriate use of AI, don’t accuse students of cheating.

Works in Theory, Wobbles in Practice

The Process: AI-Assisted Assignment Stress Testing

Seeing What You’re Not Seeing

Course Design Multiplier

Works in Theory, Wobbles in Practice

The Process: AI-Assisted Assignment Stress Testing

Seeing What You’re Not Seeing

Course Design Multiplier