- Video Tip - Rethinking Assessment in the Age of AI
- Announcements: TOPkit Online CoLAB 2025, FL-IDN webinar "Accessibility and Universal Design for Learning (UDL)"
- Top Tips - The AI Tsunami and the Future of Assessment Design
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Ask ADDIE: Demystifying OER and Open Pedagogy: A Practical Guide
- From the Community - Curated AI Resource Hubs
- Top Community Topics
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This video explores how AI tools perform complex academic tasks, from answering quiz questions to writing essays and creating presentations. Jason Tangen shares data on student AI adoption, shows live examples of AI capabilities, and discusses the challenges in detecting AI-generated work. The presentation concludes with strategies for adapting our teaching and assessment methods to this rapidly evolving landscape. It’s a candid look at how AI is reshaping education and what it means for the future of our programs.
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TOPkit Online CoLAB 2025 - Save the Date, Tuesday, July 15th, 3-4 p.m (ET). Join us for a free, fast-paced virtual speed networking event designed to help you build meaningful connections, exchange ideas, and expand your professional circle. Registration opens soon!
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FL-IDN webinar - Accessibility and Universal Design for Learning (UDL). Guest speaker Julie Alexandrin from the University of Phoenix on Tuesday, June 10, 2025. Registration is free. Sign up for other FL-IDN webinars.
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The AI Tsunami and the Future of Assessment Design |
Reframing Assessment for Insight, Not Output |
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The rapid growth of artificial intelligence (AI) is reshaping higher education and placing renewed emphasis on the role of thoughtful assessment. As AI tools become more capable of producing sophisticated text and code, educators are called to design assessments that prioritize process, originality, and critical thinking rather than focusing solely on output. This shift challenges us to examine what we are asking learners to demonstrate and whether our assessments truly reflect those goals.
Preserving the integrity of assessment does not require eliminating AI. Instead, we can design tasks that invite reflection, demand human judgment, and foster originality. These approaches not only resist automation but also support deeper learning and more meaningful engagement.
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- Promote Academic Integrity Through Purposeful Assessment Design. James Lang’s Cheating lessons: Learning from academic dishonesty reminds us that well-designed assessments can reduce the likelihood of academic dishonesty by fostering clarity, relevance, and student ownership.
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To support these outcomes, we can:
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Design tasks that are clearly connected to course goals so students recognize the purpose and value of what they are being asked to do.
- Incorporate frequent, low-stakes assessments, that provide opportunities for practice, feedback or, and revision.
- Communicate expectations transparently and include space for reflection and improvement, helping students see assessment as a learning opportunity rather than a judgment.
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When learners feel supported, know what is expected, and see how the task connects to their growth, they are more likely to approach their work with integrity.
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Prioritize Human Thinking in Assessment Design. The Every Learner Everywhere student panel offered real-world perspectives during the "campfire" discussion. Their perspectives reminded us that students are guides for designing learning experiences that are inclusive, relevant, and grounded.
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To foster this kind of engagement, we can:
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Ask learners to apply concepts in unfamiliar or ambiguous contexts where interpretation and decision-making are essential.
- Design tasks that require personal perspective, ethical reasoning, or emotional nuance, which AI cannot genuinely replicate.
- Emphasize process over product by incorporating reflection, justification, and revision as part of the assessment experience.
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When assessments require original thinking, context awareness, and meaning-making, they remain anchored in distinctly human capabilities.
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Use AI to Enhance Assessment Strategies. AI introduces challenges to traditional assessment, but it also offers practical ways to support more flexible, consistent, and meaningful evaluation.
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We can take advantage of these opportunities to:
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Use AI to generate parallel forms of assignments or quizzes that assess the same learning outcomes through different scenarios, data sets, or prompts.
- Vary the context or format of tasks without changing their cognitive demands to maintain fairness and reduce the likelihood of unauthorized collaboration.
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Adapt assessments for different delivery formats or timeframes while preserving core expectations, giving us more flexibility without increasing workload.
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When used strategically, AI can extend the reach of well-designed assessments and reinforce clarity, consistency, and responsiveness in how learning is measured.
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Center Assessment on Reflection, Revision, and Growth. Assessment should be approached as a process, not a one-time event. When it is designed to support ongoing learning, it becomes a more effective and meaningful tool for both learners and instructors.
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These goals take shape when we can:
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Use AI tools to provide early feedback and highlight patterns that may need attention, helping instructors focus their support where it matters most.
- Design opportunities for learners to revise their work based on feedback, promoting deeper reflection and ownership of learning.
- Incorporate structured self-assessment and peer review to help learners track their progress and make intentional improvements over time.
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When assessment is iterative and reflective, it reinforces learning rather than simply measuring it.
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Align Assessment with Models That Foster Authentic Learning. AI can reinforce instructional models that already emphasize authentic, student-centered learning. Rather than replacing thoughtful pedagogy, it can serve as a tool that supports deeper engagement and more flexible assessment design.
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Universal Design for Learning: AI can support multiple modalities for student expression—written, visual, or auditory—while maintaining consistent learning goals. This supports different student preferences and strengths while maintaining consistent learning goals.
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Problem-Based Learning: AI can assist in building rich, layered scenarios that challenge learners to collaborate, apply knowledge, and think critically within real-world contexts.
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Open Pedagogy: Learners can use AI to co-develop assessment questions, rubrics, or learning materials, positioning them as contributors to the course rather than passive participants. This approach encourages shared authority, deeper engagement, and a sense of purpose beyond the classroom.
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Each of these models benefits from AI’s capacity to expand access to tools and content while keeping the focus on interpretation, creativity, and purposeful application.
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Design for Growth, Relevance, and Integrity
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AI challenges us to reconsider the purpose of assessment. With intentional design, assessments can become more human-centered, more adaptable, and more meaningful. Rather than focusing solely on isolated outputs, these opportunities can highlight how learners think, make connections, and reflect on their learning.
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In an AI-infused environment, effective assessment is:
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Anchored in clear goals and authentic demonstrations of learning
- Structured to engage learners in ongoing inquiry and thoughtful iteration
- Informed by tools that support timely feedback and instructional responsiveness
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Assessment in the age of AI is not defined by limitation but by a renewed focus on insight over output.
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Demystifying OER and Open Pedagogy: A Practical Guide |
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Dear ADDIE,
I am an instructional designer at a large institution, and we assist faculty in understanding the differences between Open Educational Resources (OER) and Open Pedagogy for use in their online and blended course designs. I have found that many of our instructional designers are unsure of how these two concepts work together in the OER environment. Can you clarify these distinctions and offer some suggestions for the use of Open Pedagogy? Read more →
Signed,
Mystified in OER-Land
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TOPkit AI Resource Hub is a curated collection of resources shared by members of the TOPkit Community to support the integration of artificial intelligence in online teaching and learning. The resources help faculty developers, instructional designers, and academic leaders make informed decisions about incorporating AI in ways that enhance instruction, uphold academic integrity, and align with institutional goals. Each entry includes a direct link and a brief description to quickly find the guidance most relevant to individual needs.
- The Teaching Repository of AI-Infused Learning (TRAIIL) resource also provides a curated collection of openly licensed, peer-reviewed strategies that use generative AI tools to enhance higher education. These resources are available for designers to adopt or adapt in alignment with their specific learning objectives.
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Generative AI may have been used to retrieve relevant research, generate suggested language, and enhance original content.
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Bren Bedford, MNM, SFC®, Web Project Analyst II, Center for Distributed Learning, University of Central Florida
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Florence Williams, Ph.D., Associate Instructional Designer, Center for Distributed Learning, University of Central Florida
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