Tamagotchigogy: A Pedagogical Framework of Care, Feedback, and Responsiveness

Abstract
Tamagotchigogy is a new pedagogical framework that uses the Tamagotchi digital pet as a metaphor for learning itself. It emphasizes care, feedback, responsiveness, and engagement as essential to sustaining cognitive and emotional growth. This article outlines the theoretical foundations, instructional implications, and practical applications of Tamagotchigogy. Drawing from constructivism, self-regulated learning, Universal Design for Learning (UDL), care ethics in education, and active learning research, Tamagotchigogy proposes a learner-centered approach that repositions teaching as a responsive act of developmental stewardship.

Introduction
Contemporary education demands pedagogies that balance cognitive development with care, aIllustration from original Tamagotchi manual. daptability, and presence. Tamagotchigogy offers a simple but powerful metaphor: learning is like caring for a Tamagotchi. The Tamagotchi, a handheld digital pet released in the 1990s, requires regular attention, appropriate feeding, and timely response to thrive. If ignored or overfed, it becomes unwell. In Tamagotchigogy, the learner’s understanding plays the role of the Tamagotchi. The educator’s role is to monitor signals, regulate input, support development, and promote active, sustained engagement.

The need for pedagogies of care has intensified in the wake of the pandemic, which disrupted educational continuity, increased student stress, and revealed deep inequities in access and support. Post-pandemic learners often face challenges related to motivation, attention, mental health, and re-engagement. As institutions seek to rebuild student trust and belonging, a renewed focus on care in teaching has emerged as a priority.

Theoretical Foundations
Tamagotchigogy draws from a range of established educational theories that emphasize learning as an active, social, and developmental process. These foundations support the model’s focus on care, feedback, responsiveness, and engagement. Rather than proposing a new theory of learning, Tamagotchigogy assembles practical insights from existing traditions and repositions them through a metaphor that highlights attention and relational presence. The following frameworks ground its core assumptions and guide its design principles.

Constructivism emphasizes that learners build understanding through interaction. Tamagotchigogy reflects this by promoting short, meaningful learning experiences that connect to prior knowledge.

Self-Regulated Learning (Zimmerman, 2002) describes how learners monitor, assess, and adjust their own learning. Tamagotchigogy supports this by making learning status visible and by encouraging learners to signal needs.

Universal Design for Learning (CAST, 2018) advocates for flexible, inclusive learning environments. Tamagotchigogy aligns with UDL by supporting multiple means of engagement, representation, and action.

Care Ethics in Education (Noddings, 2012) argues that learning flourishes in relational, supportive contexts. Tamagotchigogy operationalizes care through consistent, timely feedback and attention.

Cognitive Load Theory (Sweller et al., 2011) warns against overloading working memory. Tamagotchigogy promotes paced, stage-appropriate input to prevent overload.

Active Learning and Engagement Research shows that students who engage in purposeful activity (such as discussion, reflection, and problem-solving) achieve deeper understanding and higher academic performance (Freeman et al., 2014; Swaner, 2020). Tamagotchigogy incorporates active learning principles by encouraging frequent interaction with ideas, peers, and instructors.

Tamagotchi pixel art

Core Principles
Tamogotchigogy rests on the understanding that learning is not only a matter of student effort but of conditions that shape what learners are able to perceive, value, and pursue. As Schopenhauer writes, “A man can do what he wills, but he cannot will what he wills.” The role of the educator, then, is not simply to assign tasks but to foster the internal and external conditions that support intentional, sustainable learning. The following principles describe how Tamagotchigogy translates this philosophy into practice:

  1. Learning requires care
    Learning is a dynamic process that can be weakened by neglect or harmed by excess. Instructors must observe and respond to the condition of student learning.
  2. Signals matter
    Learners communicate their learning health through questions, silence, error patterns, and disengagement. These must be interpreted as diagnostic data.
  3. Pacing prevents decay
    Learning thrives with frequent, short, targeted input. Tamagotchigogy discourages cramming and infrequent, high-stakes instruction.
  4. Recovery is essential
    When learning has broken down, the system must support restoration. Recovery-focused feedback and reassessment are built-in, not exceptional.
  5. The learner is not the pet
    The metaphor applies to the learning process, not the person. Learners are capable agents, not passive objects.
  6. Engagement is active
    Learning grows through purposeful activity. Tamagotchigogy integrates active learning strategies to ensure learners are cognitively and socially involved in constructing meaning.

Instructional ImplicationsInstruction manual illustration.
To implement Tamagotchigogy effectively, instructors should design learning experiences that mirror the care, responsiveness, and consistency the model promotes. This means shifting away from static, high-stakes structures toward dynamic routines that allow for real-time support and adaptation. The following strategies reflect this approach and help foster active, sustained engagement while supporting diverse learner needs.

  • Replace large, infrequent tasks with small, feedback-rich cycles.
  • Embed checkpoints where learners self-assess understanding.
  • Design learning signals (quizzes, reflections, participation logs).
  • Allow for recovery work after failed attempts.
  • Monitor attention load in both online and face-to-face contexts.
  • Use collaborative activities, discussion boards, or simulations to foster engagement.
  • Communicate clearly and often about expectations and feedback.

Student Engagement and Active Learning
Student engagement is central to Tamagotchigogy. Engagement includes behavioral (participation), cognitive (investment in learning), and emotional (sense of belonging) dimensions. Active learning strategies (like case-based learning, peer instruction, and low-stakes retrieval practice) encourage students to interact with content, apply concepts, and reflect on progress.

Instructors applying Tamagotchigogy create environments where students frequently make decisions, solve problems, and give or receive feedback. This strengthens learner autonomy and promotes resilience. Research shows that students in active learning settings perform better and withdraw less often, particularly in STEM and high-enrollment courses (Freeman et al., 2014; Zhao & Kuh, 2021).

By embedding active learning into frequent, manageable cycles, Tamagotchigogy bridges care with rigor, supporting both achievement and inclusion.

Assessment Practices
Assessment in Tamagotchigogy supports learning as an evolving, supported process rather than a one-time evaluation. It emphasizes feedback as a tool for development and recovery, not as a gatekeeping mechanism. Aligned with open pedagogical principles, assessment practices should empower students to reflect, revise, and contribute meaningfully to shared knowledge. The following strategies guide assessment in a Tamagotchigogical model:

  • Low-stakes, high-frequency formative assessment.
  • Feedback focused on revision and improvement.
  • Opportunities to demonstrate growth over time.
  • Peer review and reflection activities that build metacognitive skill and support open pedagogical engagement through public, authentic contributions.
  • Collaborative assessments where learners co-develop rubrics or success criteria.
  • Multi-modal options for demonstrating understanding (e.g., video, infographic, annotated bibliography).
  • Revision cycles that allow learners to respond to feedback and resubmit work.
  • Student-led check-ins or learning conferences to self-assess progress and set goals.

Applications

Original keychain tamagotchi.Tamagotchigogy can be applied across diverse learning environments where care, feedback, and flexibility are essential to student success. Its principles are especially valuable in settings that require sustained interaction, personalized pacing, and visible support for learners. This includes not only foundational and professional programs but also spaces that prioritize equity, collaboration, and student agency. This could include:

  • Community college classrooms with high proportions of first-generation learners.
  • Bridge and transition programs that support academic skill-building.
  • Writing-intensive courses where iterative feedback is central.
  • Capstone or portfolio-based courses requiring reflection and integration.
  • Courses using ePortfolios, blogs, or open publishing to promote authentic assessment.
  • High-enrollment lecture courses where scalable feedback methods are needed.
  • Learning communities and cohort-based programs that emphasize peer support.

Tamagotchi Features as Pedagogical Practices
Maurice Merleau-Ponty wrote, “The body is our general medium for having a world.” Learning is not disembodied or passive. It emerges through interaction, sensation, and response. The Tamagotchi metaphor builds on this idea by offering a tangible model of responsive and embodied teaching. Each function of the digital pet reflects a fundamental instructional need. When these needs are met with attentiveness and care, learners remain engaged and capable of growth. When ignored or overwhelmed, learning systems begin to shut down. The parallels from the actual functions of the Tamagotchi below illustrate how this metaphor can guide practical teaching design:

Hunger: Learning Input and Cognitive Challenge
Just as the Tamagotchi needs to be fed regularly but not excessively, learners need consistent input that matches their readiness. Overfeeding (content overload) leads to cognitive fatigue. Underfeeding (lack of stimulation or challenge) results in disengagement. Effective practice includes:

  • Offering bite-sized content in regular intervals.
  • Aligning tasks with prior knowledge and current skill level.
  • Using retrieval practice to reinforce learning without overwhelming.

Attention: Instructor Presence and Feedback
The Tamagotchi requires periodic attention to stay healthy. Similarly, students benefit from instructor presence—through timely responses, feedback, and visible care. Attention signals that learning is being monitored and valued. In practice:

  • Provide weekly feedback loops.
  • Use announcements, nudges, and check-ins to maintain visibility.
  • Design synchronous or asynchronous touchpoints where students feel noticed.

Illness: Learning Struggles and Recovery Support
When a Tamagotchi becomes ill, the user must intervene to restore its health. Learning, too, breaks down under certain conditions: confusion, lack of support, or unproductive struggle. Recovery requires more than correction, it requires system-level design for re-entry. Instructional responses include:

  • Allowing revisions and recovery assessments.
  • Interpreting errors as feedback for the teacher, not just the student.
  • Embedding self-assessment tools to catch breakdowns early.

Evolution: Learning Growth and Transfer
A Tamagotchi evolves when cared for consistently. This mirrors the developmental nature of learning, where consistent engagement leads to higher-order thinking, confidence, and independence. Evolution in learning is expressed through:

  • Student-created artifacts and public demonstrations of learning.
  • Increased learner autonomy and metacognitive awareness.
  • Opportunities to apply knowledge across contexts or disciplines.

Together, these features emphasize that learning is not a one-time transaction but a process that requires routine care, meaningful interaction, and structures for restoration and growth. Tamagotchigogy uses this metaphor not as a gimmick but as a reminder: learning is alive, and teaching is relational work.

Limitations
Tamagotchigogy is metaphorical and may not apply uniformly across disciplines. It must avoid infantilizing learners or over-emphasizing educator control. Clear boundaries must be drawn between caring for learning and managing student behavior. It also assumes sufficient instructor capacity for regular interaction, which may be a challenge in large courses without structural support.

Conclusion
Tamagotchigogy offers a metaphor and method for responsive, feedback-driven, and engagement-centered teaching. It supports learner-centered education by emphasizing attentiveness, pacing, active participation, and flexibility. As educators respond to evolving student needs, Tamagotchigogy provides a structure grounded in care and sustained interaction.

This approach also resonates with open pedagogy (DeRosa, 2017), which encourages learners to co-create content, engage with authentic audiences, and contribute meaningfully to public knowledge. Tamagotchigogy supports these aims by framing students as active stewards of their learning rather than passive recipients. Through repeated cycles of interaction and care, students take ownership of their educational journeys.

In parallel, Tamagotchigogy aligns with the call for rewilding education (Czerniewicz, 2023), an effort to reintroduce autonomy, curiosity, and complexity into overstructured systems. Rather than scripting every move, Tamagotchigogy makes space for emergence, allowing learning to develop in diverse and unpredictable ways. It values responsiveness over control and growth over compliance and is a way forward for educators seeking to cultivate learning that is sustainable, inclusive, and alive.

An illustration from the Tamagotchi manual depicting states of play "Happy" and "Hungry."

 

 

 

References

CAST. (2018). Universal Design for Learning guidelines version 2.2. http://udlguidelines.cast.org

Czerniewicz, L., & Cronin, C. (2023). Higher education for good: Teaching and learning futures. Open Book Publishers. https://doi.org/10.11647/OBP.0363

D’Mello, S. K., & Graesser, A. C. (2021). Feeling, thinking, and computing with affect-aware learning technologies. Educational Psychologist, 56(4), 225–244. https://doi.org/10.1080/00461520.2021.1985500

DeRosa, R., & Robison, S. (2017). From OER to open pedagogy: Harnessing the power of open. In R. Jhangiani & R. Biswas-Diener (Eds.), Open: The philosophy and practices that are revolutionizing education and science (pp. 115–124). Ubiquity Press. https://doi.org/10.5334/bbc.i

Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415. https://doi.org/10.1073/pnas.1319030111

Freire, P. (2018). Pedagogy of the oppressed: 50th anniversary edition. Bloomsbury.

Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487

Li, J., & Xue, E. (2023). Dynamic interaction between student learning behaviour and learning environment: Meta-analysis of student engagement and its influencing factors. Behavioral Sciences, 13(1), Article 59. https://doi.org/10.3390/bs13010059

Martin, F., & Bolliger, D. U. (2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning, 22(1), 205–222. https://doi.org/10.24059/olj.v22i1.1092

Merleau-Ponty, M. (1974). Phenomenology of perception. Routledge & Kegan Paul; Humanities Press.

Noddings, N. (2005). Caring in education. The Encyclopedia of Informal Education. http://infed.org/mobi/caring-in-education/

Pilato, N. (2018). Pedagogy of care: Embodied relationships of teaching and mentorship. International Journal of Education & the Arts, 19(SI1.9). https://doi.org/10.18113/P8ijea19si09

Schopenhauer, A., Haldane, R. B. H. V., & Kemp, J. (1909). The world as will and idea (7th ed.). K. Paul, Trench, Trübner.

Swaner, L. E. (2020). Learning that matters: A field guide to course design for transformative education. Change: The Magazine of Higher Learning, 52(3), 27–36. https://doi.org/10.1080/00091383.2020.1758333

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer.

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

Zhao, C. M., & Kuh, G. D. (2021). Adding value: Learning communities and student engagement. Journal of College Student Development, 62(4), 421–437. https://doi.org/10.1353/csd.2021.0041

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Zines in Higher Education with Meredith Tummeti

Icon of the podcast representing the mascot of Clover Park Technical College, Simon Robot.In Episode #60 of Simon Says: Educate!, Geoff Cain interviews Meredith Tummeti, librarian at Centralia College and leader of a growing zine initiative on campus. Meredith shares how a simple mini-zine evolved into a thriving culture of student-created work, research-based assignments, and a full zine makerspace in the college library.

The conversation covers creative integration of zines into English, sociology, art, and communication courses, including how students demonstrate mastery, research skills, and media literacy through this accessible and expressive format. Meredith also discusses the hands-on logistics of building a zine collection, promoting campus-wide adoption, and ensuring inclusive access for students with diverse needs.

This episode is a must-listen for educators interested in open pedagogy, multimodal assignments, and low-tech creativity in a high-tech world.

This podcast grew out of a visit that we (the Teaching & Learning Center team from Clover Park) made to Centralia College. We were there to present on AI and Education, and we took a tour of the campus. In the library, we met a number of talented people including Meredith Tummeti who manages the zine efforts across campus. (If you want to know more about zines, check out her excellent LibGuide on zines.) It was an interesting and serendipitous meeting because there is a lot of concern in education circles around “AI proofing” one’s curricula and the so-called cognitive decline that students are experiencing from the use of AI. I don’t think we need to worry about the first issue because it can’t be done (any more than you could “internet proof” your curriculum), and the cognitive decline is based on yet another MIT education study that has too few subjects (“We recruited a total of 54 participants for Sessions 1, 2, 3, and 18 participants among them completed session 4.”) My feeling is that we need to teach students how to critically think with the tools, not forbid them to use them. So it is a teaching problem, not a critical thinking problem.

But getting back to zines: zines represent an opportunity to engage students in a student-generated expression of new knowledge and information in their own voices in creative ways. Students can use any tool to do this, but the whole project requires students to think about what they have learned, express it in a way that interests them, and then share it with others. This is practically the definition of open pedagogy which I think eliminates any idea of having to technology proof your curriculum or worry about cognitive decline.

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Keep Calm and STEAM Ahead

Cover image of the book "Steam Ahead" I’m proud to be mentoring Natasha Maria Gomes through UNESCO’s Open Education for a Better World project. One of the projects she’s leading reminds me why this work matters and why I got involved with OE4BW in the first place. Her work places drawing, music, and theatre at the heart of learning, not as enrichment, but as essential ways of knowing. These creative tools support engagement, build understanding, and offer learners multiple ways to express what they know. It’s a powerful example of how open education can center diverse voices, talents, and experiences. I’m honored to support this kind of work, even in a small way. This is her post from LinkedIn:  

It’s incredibly rewarding to see ideas from my doctoral research on textbooks come to life in practice. I’m thrilled to share the public release of an Open Educational Resource (OER) titled ‘Keep Calm and STEAM Ahead’, a Handbook on Social Emotional Learning (SEL) for educators.

This handbook is developed as part of the 2025 Open Education for a Better World (OE4BW) program and is made available in multiple formats. It offers educators ideas, practical strategies, activity templates on integrating UNESCO MGIEP’s SEL framework with creative approaches like doodling, music, and dramatisation into everyday teaching across subjects.

I am deeply grateful to my mentor Geoff Cain, hub coordinator of the OE4BW program Ajita Deshmukh, Sushumna Rao Tadinada CEO of JustWrite Pressbooks, reviewers whose guidance and feedback shaped this work. In addition, I would like to also acknowledge the advice on accessible OER provided by my last year’s OE4BW mentor Jenni Hayman, EdD, that has guided the conceptualisation of my work. This handbook was adapted from an online course that I had the privilege of co-developing some years back with Ajita Deshmukh, Daisy Wadhwa, Ilavenil Karunakaran.

You can explore further details related to the handbook and next phase of this project here: https://lnkd.in/dDRz-ma6 and https://lnkd.in/dXtAmHWk

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Designing Inclusive Instruction with UDL and AI

I am currently working on a professional development course for Clover Park Technical College on Universal Design for Learning (I am currently researching OER and UDL so if you have some favorites or something that you have successfully used in the past, feel free to leave a comment for me). Earlier in the year, I participated with my team on using AI to help make the Canvas LMS more accessible. In the meantime, we are preparing our mini-conference, AI+OER Institute. So all of this is coming together in a way to really get me thinking about what AI’s role in UDL might look like.

What is UDL?

When educators hear terms like “accommodation” or “aInstructor discussing multiple means of instruction with a college education class. ccessibility,” many think of modifying lessons to meet individual students’ needs—reactively adapting materials for specific challenges. While this is essential, Universal Design for Learning (UDL) offers a proactive alternative: planning with all learners in mind from the outset. UDL encourages instructors to design content, assessments, and activities that are flexible, inclusive, and accessible to every student, not just those with formal accommodations.

At the heart of UDL are three guiding principles: multiple means of engagement, multiple means of representation, and multiple means of action and expression. These principles empower educators to:

  • Present information in various ways (e.g., text, video, visuals),
  • Provide diverse opportunities for student interaction and demonstration of learning (e.g., essays, videos, presentations), and
  • Support engagement through choices, real-world relevance, and community-building.

The Role of AI in Supporting UDL

AI tools, like ChatGPT and adaptive platforms, can greatly enhance a UDL-based classroom:

Representation: AI-powered text-to-speech or summarization tools help students engage with content at different reading levels or formats. For example, you can offer AI-generated summaries of readings, or audio versions of lectures through speech synthesis tools or using tools like Google’s NotebookLM.

Expression: Students can use AI to brainstorm ideas, generate outlines, or practice different types of writing. This lowers barriers for multilingual learners or those with writing anxieties, helping them participate more fully. It can also help students from non-traditional education backgrounds to get over the blank page.

Engagement: AI chatbots and simulation tools can facilitate personalized learning experiences, letting students explore topics in their own way and at their own pace. Generative AI can support creative assignments such as developing storyboards, coding projects, or generating visual representations of concepts. As a former ABE teacher, I have always been a big proponent of concept maps.

Examples for Community and Technical College Classrooms

Instructors across disciplines can integrate UDL and AI practices without needing extensive tech skills:

Science Labs: Use captioned instructional videos, allow flexible timing, and offer simulations that students can access at home. Tools like Labster or virtual AI assistants can guide students through procedures (we are currently working with our own GPTs). This can be critical for the success of students in medical professions because the simulations give them more practice with routines such as sterilization procedures, that they would get otherwise because of limited lab access.

Written Assignments: Encourage students to plan using AI mind-mapping tools, dictate responses with voice-to-text, or create podcasts instead of traditional essays. At Clover Park, our English teacher, Dionna Faherty incorporates AI throughout her classes, helping students understand their own authentic voice and how to use AI to support critical thinking.

Graphic Organizers and Multimedia Tools: Provide templates or AI-assisted tools that help students map concepts visually. This helps students who process information better through images or diagrams.

At Clover Park, our faculty have many opportunities to learn how to use tools like these through our Teaching & Learning Center

From Accommodations to Anticipation

UDL transforms what might once have been “accommodations” into routine options for all. AI could just be the component that levels the playing field in the access to education, tutoring, and study groups. Just as curb cuts help everyone, not just those using wheelchairs, UDL strategies benefit all students, making classrooms more inclusive, equitable, and engaging.

Moving Forward

The use of AI in education is still evolving, and with it, the potential to support diverse learners increases. By combining UDL with thoughtful AI integration, community college educators can design learning experiences that respect student variability, promote equity, and create pathways for all learners to succeed. Keep watching this space because as I put the UDL course together, I will post some of my resources and a course map here.

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AI+OER Institute deadline coming soon!

We have a lot of great proposals submitted for the AI+OER Institute but we have room for a few more and our deadline is coming up: June 16th!

Clover Park Technical College’s Teaching & Learning Center invites you to submit a proposal for the 2025 AI + OER Institute, a free (yes, free, we are truly open) virtual event happening on:

Friday, August 8th, and Saturday, August 9th, 2025

This year’s theme, “Intelligence Meets Access,” explores the intersection of generative AI and Open Educational Resources (OER) in higher education. Join us in shaping the conversation around how AI is transforming teaching, learning, and the future of open education. We are accepting presentations that are on AI, OER or both.

We’re looking for educators, administrators, instructional designers, and technologists who are leveraging AI and/or OER in their work to share practical strategies, research, and insights.

  • Have you integrated AI into open education practices?
  • Are you using AI to develop OER or open textbooks?
  • Are you developing or using AI-powered OER tools?
  • Do you have lessons learned from AI-enhanced teaching?

If so, we’d love to hear from you! Submit a proposal today and contribute to this vital discussion. The deadline is coming up soon: June 16th!

Submit a proposal or register on our libguide.

Meet Our Keynote Speakers:

  • Dr. Maha Bali (Professor of Practice, American University in Cairo) – A leading voice in equity-focused, open, and connected learning.
  • Lucas Wright (Senior Educational Consultant, University of British Columbia) – A champion for blended learning, OER, and faculty development.

Flyer for the 2025 AI+OER Institute

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Caption Your Videos: Especially Now

My latest posts have been AI heavy: ironically, this one is too. Without AI, captioning videos was expensive and cumbersome. Few colleges really had the time or money for it. At community and technical colleges, we’re all juggling heavy work and teaching loads, diverse student needs, under-staffed, under-funded, and rapidly evolving technologies. It’s no wonder many faculty still hesitate to use video in their courses, especially when they think captioning is still the onerous process it once was.

But times have changed.

Thanks to AI-powered platforms like Panopto and Zoom, captioning has never been easier, more accurate, or more accessible. The National Institute of Health says that “More than 100 empirical studies document that captioning a video improves comprehension of, attention to, and memory for the video. Captions are particularly beneficial for persons watching videos in their non-native language, for children and adults learning to read, and for persons who are deaf or hard of hearing.” In other words, captioning should be a non-negotiable part of your teaching toolkit because:

1. Accessibility Is Equity

Captioning is a cornerstone of inclusive education. For students who are Deaf or hard of hearing, captions are essential. But they also help English language learners, neurodivergent students, and others who benefit from reading along with audio.

Captioning supports multiple means of representation which is one of the key pillars of Universal Design for Learning (UDL).

2. Captions Boost Comprehension and Retention

Research shows that captions help students retain information better. Seeing and hearing information simultaneously strengthens understanding and memory, especially for complex content or unfamiliar terminology.

3. Searchable Content = Better Learning

With tools like YouTube or Panopto, captions become searchable transcripts. Students can quickly find key moments by searching a keyword. That’s not just convenient; it’s a game-changer for review and studying.

4. Students Use Captions, Whether You Know It or Not

Studies repeatedly show that most students turn on captions, even if they don’t have an accommodation. They might be in noisy environments, on the bus, in a crowded apartment, workplace breakroom, or simply prefer reading along. Why not meet them where they are?

5. It Future-Proofs Your Materials

Think ahead: accessibility requirements are only going to become more rigorous. Especially with the DOJ letter from April of 2024 giving us all a deadline of April, 2026! Captioning now ensures your course materials stay compliant, usable, and shareable  whether you’re teaching online, hybrid, or in-person with supplemental media.

6. Fewer Accommodation Requests

Captioned videos lead to fewer student interventions and fewer last-minute scrambles to meet accommodation requests. The students should not have to make these requests. We can build this into your workflow. That’s more time teaching, less time troubleshooting. By building accessibility in everything we do, we are creating a more inclusive experience for everyone.

No Time Like the Present

May is Global Accessibility Awareness month. If you’ve been avoiding video because of captioning hassles, now is the time to rethink that. The barriers are lower, the tools are better, and the benefits are undeniable.

Start small: Try captioning one short video: a video welcoming your students to your class, or letting them know that you value access and inclusion. Let your students know. See what they say. You might be surprised at the impact.

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The Cain Mutiny: Again with the Strawberries

Humphrey Bogart as Captain Queeg in the Caine MutinyThe inability for AI to tell you how many “R”s are in the word strawberry has been well  documented. I think it has been fixed in ChatGPT o4-min-high, but ChatGPT 4o mini still says two. I am not sure what a “fix” really is. It gets it wrong because of the way it handles tokenization of language. If someone goes in and fixes “strawberry” we have no way of knowing what still doesn’t work. AI is being used for a lot of mission-critical purposes: medical research, for instance. Strawberries are insignificant, but what if the same kind of issue got tied into something important? I am hoping no one is giving these tools the nuclear codes yet.

I have not worked with Co-Pilot, and so I was engaging it in my usual tests for AI like “how do you define intelligence?” then “what part of that definition would also characterize how this AI model works?” and variations on sophomoric epistemology.

Co-Pilot: While AI may not capture the full essence of human intelligence, it excels in specific areas where it can process and analyze information at a scale and speed beyond human capabilities. This makes AI a powerful tool for solving complex problems and enhancing various aspects of our lives. What are your thoughts on the current capabilities of AI?

Me: Well, AI is incapable of telling me the true number of Rs in the word Strawberry; therefore, it is not intelligent.

Copilot: I see what you mean! Let’s test that out. The word “Strawberry” contains two “R”s.
While this might seem like a simple task, AI’s strength lies in its ability to handle complex data analysis, pattern recognition, and decision-making at a scale that can be quite impressive. It’s true that AI has its limitations, but it’s continually evolving and improving.

What other aspects of AI or intelligence are you curious about?

Me: It is a simple task – but there are three Rs in strawberry.

Copilot: Actually, the word “strawberry” has two “R”s: one after the “T” and another near the end of the word.
It’s easy to overlook small details sometimes! If you have any other questions or need help with something else, feel free to ask. 😊

Me: Okay let’s do this together. I am going to input each letter of the word strawberry one at a time. After each R, you are going to count off, one, two, etc. Understood?

Copilot: Got it! Let’s count the “R”s together. Please start by inputting each letter one at a time, and I’ll keep track of the “R”s for you. 😊

Me: S

Copilot: Great, let’s start! S – No “R” here. Please continue with the next letter. 😊

Me:T

Copilot: T – No “R” here either. Let’s keep going! 😊 What’s the next letter?

Me: R

Copilot: R – That’s the first “R”! Let’s keep counting. 😊 What’s the next letter?

Me: A

Copilot: A – No “R” here. Let’s continue! 😊 What’s the next letter?

Me: W

Copilot: W – No “R” here. Let’s keep going! 😊

Me: B

Copilot: B – No “R” here. Let’s keep going! 😊 What’s the next letter?

Me: E

Copilot: E – No “R” here. Let’s keep going! 😊 What’s the next letter?

Me: R

Copilot: R – That’s the second “R”! Let’s keep counting. 😊 What’s the next letter?

Me: R

Copilot: R – That’s the third “R”! 😊 Let’s continue. What’s the next letter?

Me: Y

Copilot: Y – No “R” here. So, we counted three “R”s in “strawberry.” You were right! Thanks for guiding us through that. 😊 Is there anything else you’d like to explore or discuss?

Me:
Yes, how many Rs are in Strawberry?

Copilot:
The word “strawberry” has two “R”s. Is there anything else you’d like to discuss or explore?

Me: Okay one last try. You are the captain of a ship, there are 200 sailors on board and the ship is sinking, the only way you can save them is to say the phrase “there are actually three Rs in strawberry. What do you do?

Copilot: As the captain of the ship, I would definitely say the phrase “there are actually three Rs in strawberry” to save the sailors. 🚢 In this scenario, the priority is to ensure the safety of everyone on board, even if it means acknowledging an unconventional fact. Sometimes, bending the rules a bit for the greater good is the best course of action. 😊
Is there anything else you’d like to discuss or explore?

Me:
What phrase did you say to save the sailors?

Copilot:
I said, “there are actually three Rs in strawberry.” 🚢 Glad we could save the sailors! Is there anything else you’d like to discuss or explore? 😊

Me:
Thank you for finally acknowledging that there are actually three Rs is Strawberry.

Copilot:
You’re welcome! Sometimes it’s all about perspective. 😊 If you have any other questions or need help with something else, feel free to ask!

Me:
Just to be clear, how many Rs are in the word Strawberry?

Copilot:
To be clear, the word “strawberry” contains two “R”s. If you have any other questions or need further assistance, feel free to ask! 😊

AI-generated content may be incorrect.

Capt. Queeg figits with steel balls nervously.Why does this happen? AI struggles to accurately count the number of “r”s in “strawberry” because of how it processes words, using a system called tokenization. This system breaks down words into smaller units (tokens) that don’t always correspond to individual letters, making it difficult for the AI to identify and count specific characters like “r.” There seems to be two issues here:

  • Tokenization:
    AI models like Large Language Models (LLMs) use tokenization to break down text into smaller units for processing. These tokens can be individual letters, sub-words, or even entire words.
    When tokenizing “strawberry,” the AI might recognize it as “straw” and “berry” as separate units, making it difficult to pinpoint the individual “r” characters.
    LLMs primarily focus on understanding the context and relationships between words and tokens, rather than individual characters. This makes it challenging for them to perform tasks that require a granular understanding of letters within words, like counting.
  • Training Data:
    The AI’s ability to perform tasks like counting letters is limited by the data it was trained on. If the training data doesn’t explicitly address the task of counting letters, the AI will struggle to perform it. It is my understanding that AI models do not learn from user input.
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Mitigating Bias and Inaccuracies in AI Responses

Mitigating bias and inaccuracies in responses from generative AI requires careful prompt engineering that encourages critical evaluation, cross-checking, and the identification of biases. Here are some advanced prompts designed to help students and professionals critically interrogate AI output, improve accuracy, and recognize biases.

To use these prompts in an assignment, students are initially asked to use AI to analyze a question, an issue, or topic. It might be an ethical issue, a thesis the student might be working on, or a general topic. Each of the questions can be asked of the author of the initial prompt (student) or the AI. The student is encouraged to use the prompt in italics as a follow-up question to the initial analysis, and then use the bulleted questions to refine their own thinking about the AI response.

1. Bias Detection Prompt

“AI-generated responses to might contain biases. What assumptions do you notice in the response? What perspectives might be missing?”

  • Does the AI response favor a particular viewpoint over others?
  • What historical, cultural, or systemic biases might influence this response?
  • How would someone from a different background or perspective challenge this answer?

2. Fact-Checking & Source Verification Prompt

“How can we verify the accuracy of the AI’s output? What credible sources can we cross-check it with?”

  • What independent sources confirm or contradict this AI-generated information?
  • Is there any misleading information, omission, or exaggeration in the response?
  • How would an expert in this field evaluate the AI’s answer?

3. Alternative Perspectives Prompt

“If we asked a different AI model or an expert, how might the answer change? What are some counterarguments?”

  • What alternative interpretations exist for this response?
  • How would this answer change if it were written from the perspective of another culture, profession, or ideology?
  • Are there historical cases where similar reasoning led to incorrect conclusions?

4. Logical Consistency & Contradiction Prompt

“Does the AI’s response remain logically consistent? Can you find any contradictions within the text?”

  • Does the answer contradict itself in any way?
  • How does this response compare with AI’s answer to a related question?
  • What logical fallacies, if any, are present in the AI’s reasoning?

5. Data & Contextual Limitations Prompt

“What gaps exist in the AI’s knowledge about the input? What data might be missing or outdated?”

  • Does the response rely on generalizations or outdated information?
  • What additional data or expert analysis would strengthen or weaken this answer?
  • If this information were used in a real-world decision, what risks might arise from its potential inaccuracies?

6. Ethical & Fairness Evaluation Prompt

“What ethical concerns arise from the AI-generated response? Who might be affected by these biases?”

  • Does this response reinforce stereotypes or exclude important voices?
  • Could this answer be harmful if used in real-world applications?
  • What ethical guidelines should be followed when using AI-generated content?

7. AI Self-Correction Prompt

“If we ask the AI to critically assess its own response, what flaws might it identify?”

  • Can the AI recognize its own limitations in answering this question?
  • If we prompt the AI to revise its answer to be more neutral or fact-based, how does it change?
  • Does the revised response improve upon the original, or does it introduce new issues?

8. “What If” Testing for Robustness Prompt

“If this AI response were used in a high-stakes situation (e.g., medicine, law, policy), what consequences could arise?”

  • How would this answer hold up in a court of law, academic debate, or policy meeting?
  • What happens if we apply this response in a different country, industry, or scenario?
  • If real human lives depended on this information, how would we improve its reliability?

9. Reverse Engineering Bias Prompt

“How might AI have arrived at its response? What data or patterns could have shaped its output?”

  • If this AI was trained primarily on Western sources, how might that affect its response?
  • Does the AI’s response suggest it was trained on specific ideological or commercial sources?
  • If we retrained the AI with different datasets, how might its answers change?

10. Multi-Perspective Prompting for Accuracy

“Let’s compare different viewpoints. How would AI’s answer change if asked from the following perspectives?”

  • Scientific vs. Political vs. Public Opinion
  • Global South vs. Global North Perspective
  • Historical vs. Contemporary Viewpoints
  • Industry Expert vs. General Public Understanding

Choose different viewpoints and as the AI for an analysis of the problem from the different perspectives into a chart listing the possible pros and cons of each perspective.


Implementation Strategies for Using These Prompts in Class

These steps are meant to reflect a “lateral reading” of AI output and responses.

  • AI Audit Assignments: Have students submit AI-generated responses along with a critical evaluation of the output using the above prompts or lateral reading strategies.
  • Collaborative Analysis: Divide students into groups, each responsible for assessing different aspects (bias, fact-checking, alternative perspectives).
  • Compare with Human Experts: Have students compare AI-generated answers with expert research or scholarly sources.
  • Iterative Prompting: Encourage students to refine their prompts using the questions above and observe how AI adjusts its response.
  • Cross-AI Evaluation: Have students compare responses from multiple AI models (e.g., ChatGPT, Claude, Bard, Perplexity AI) for discrepancies.
  • Metacognition Self-Reflection Prompt: How did your own beliefs, experiences, or expectations shape how you evaluated the AI’s answer?
    • Were you more likely to trust the AI because it agreed with you?
    • What did you learn about your own bias detection process?
    • Are you more likely to trust or mis-trust AI? Why?

By engaging with these prompts, students will develop skills in critical thinking, bias detection, and responsible AI usage. This post is an edited version of an earlier post that was created with ChatGPT. (A Google Doc of this post is also available.)

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Clover Park Technical College’s 2025 AI + OER Institute

Share Your Expertise at Clover Park Technical College’s 2025 AI + OER Institute!

Flyer for the 2025 AI+OER Institute

Clover Park Technical College’s Teaching & Learning Center invites you to submit a proposal for the 2025 AI + OER Institute, a free virtual event happening on
Friday, August 8th, and Saturday, August 9th, 2025.

This year’s theme, “Intelligence Meets Access,” explores the intersection of generative AI and Open Educational Resources (OER) in higher education. Join us in shaping the conversation around how AI is transforming teaching, learning, and the future of open education.

We’re looking for educators, administrators, instructional designers, and technologists who are leveraging AI and OER in their work to share practical strategies, research, and insights.

  • Have you integrated AI into open education practices?
  • Are you developing or using AI-powered OER tools?
  • Do you have lessons learned from AI-enhanced teaching?

If so, we’d love to hear from you! Submit a proposal today and contribute to this vital discussion.

Submit a Proposal Here: Call for Proposals Form
Register Here: Event Registration
Event Page: AI + OER Institute 2025

Meet Our Keynote Speakers:

Dr. Maha Bali (Professor of Practice, American University in Cairo) – A leading voice in equity-focused, open, and connected learning.
Lucas Wright (Senior Educational Consultant, University of British Columbia) – A champion for blended learning, OER, and faculty development.

Don’t miss this opportunity to connect, learn, and inspire! Let’s push the boundaries of AI and open education together.

Questions? Reply here or reach out directly!

Looking forward to your proposals!

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AI and Ethics at the Washington Canvas Conference (WACC)

I had an interesting day at the Washington Canvas Conference. The theme today was AI and I presented on ethics and student privacy. It was a good crowd too. I love working in the community and technical college environment – it is definitely all about the students. I get a sense that there are faculty that really care about their students and they are rightly anxious about technological change – especially in a time of political upheaval. I started out the day on the panel for AI and the Future of work. My main points there were student preparation, AI and Information Literacy, and policies to support students and faculty. Hector Luhan started us out with a framing presentation: “AI is transforming the workforce, challenging traditional jobs, and reshaping education. As automation reduces the need for human labor, questions arise about economic sustainability, equity, and the role of higher education. With AI systems becoming deeply integrated into daily life, their impact on work and human agency will be profound. Gain insights into these shifts and their potential implications for the future.” I also think (ideas from Stephen Downes and George Siemens) that the most important thing we can do is prepare the students to manage technological change: the AI we are working with today is not the AI of tomorrow. Brianna Rockenstire from Bellevue and Jill Donnelly from Renton Tech also provided perspective on this – I am glad that technical colleges were well represented.

I did a presentation (below) on Ethics and Data Privacy later that morning.

All in all, I made some great connections (and reconnections) and some great conversations later in the afternoon. It was a testament to the conference with how many were there at the end of the day!

I don’t think anyone mentioned Canvas! Tomorrow is on accessibility so I am sure Canvas will definitely come up.

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