Creativity in the Age of AI

In Collaboration in the Age of AI, we explored how students learn through shared thinking, problem-solving, and dialogue. Collaboration and creativity are closely connected because working with others can help ideas emerge, develop, and take new directions. Generative AI can influence this process by giving students access to ideas, examples, and possibilities almost instantly.

This raises an important question for educators: How can generative AI support creative thinking while helping students maintain originality, expression, and ownership of their work? As with the other practices explored in this series, the answer begins with pedagogy and with a clear understanding of what we want students to learn.

Why Creativity Matters

Creativity is more than producing something new or polished. In teaching and learning, creativity can involve generating possibilities, making connections, approaching a problem from different directions, experimenting with ideas, and revising those ideas over time. It also requires students to make choices about what works, what does not, and why.

Generative AI can contribute to this process by offering ideas or alternatives students may not have considered. At the same time, having more ideas available does not necessarily mean that students are thinking more creatively. Research suggests that generative AI can influence different aspects of student creativity, including originality, flexibility, and idea development, but its effects are not always straightforward (Habib et al., 2024). This makes it important to consider not only what AI can generate, but what students are doing with what it generates.

Creativity as a Process

Creative learning takes time. An initial idea may be questioned, combined with another idea, changed, or even abandoned as students develop their thinking. Generative AI can speed up the beginning of this process, but generating an idea is not the same as engaging in the creative process.

One way to think about that process is:

Generate → Question → Select → Develop → Revise → Create

These steps are not meant to prescribe how every creative activity should unfold. Instead, they illustrate the thinking and decision-making that can occur between an initial idea and a final product. Students might generate several possibilities with AI, question their usefulness, select an idea worth pursuing, develop it through their own thinking, and revise it as their understanding grows.

How educators structure these experiences matters. Research suggests that students' creative and critical thinking can be strengthened when the use of generative AI is paired with purposeful learning strategies rather than simply providing access to the technology (Chiu & Hwang, 2025). This reinforces a familiar principle from this series: the tool itself does not determine the quality of the learning experience. The design of the activity does.

Using GenAI to Support the Creative Process

Generative AI can support creativity at different points in the learning process. The goal is not for AI to do the creative work for students, but to give them something to explore, question, develop, or transform.

Idea Exploration: Students might use AI to generate several possible approaches to a topic, question, or problem. Rather than choosing one immediately, they can evaluate the suggestions, identify what is useful or missing, and develop a direction of their own.

Creative Problem-Solving: Students can compare their ideas with AI-generated alternatives. They might consider the strengths and limitations of each approach, combine useful elements, or explain why they chose a different solution. The emphasis remains on reasoning and decision-making rather than simply producing an answer.

Revision and Transformation: Students might begin with an AI-generated example, draft, image, or concept and then revise or transform it. Asking students to explain what they changed and why can make their creative decisions more visible and keep them actively involved in shaping the final work.

Across these examples, generative AI provides possibilities, but students remain responsible for deciding what to do with them. That distinction helps keep the focus on creative thinking rather than AI-generated products.

Originality, Expression, and Student Voice

The availability of generative AI also invites educators to reconsider what originality means. Creative work has never developed in isolation. Students draw from course content, previous experiences, conversations, examples, and the ideas of others. Generative AI becomes another possible influence, but one that can produce complete ideas and products with very little effort from the student.

Originality, then, may be less about creating something without outside influence and more about the choices students make as they develop their work. Students can bring their own perspectives, decide which ideas to accept or reject, make connections that are meaningful to them, and shape the final work in ways that reflect their thinking and voice.

Recent research with students engaged in creative work found that they valued AI for activities such as brainstorming and getting started, but were more cautious about relying on it for aspects of creative work involving personal voice, artistic judgment, and expression (Duong, 2026). This distinction is important. AI can contribute to the process without becoming the source of the student's creative identity.

Educators can support originality by designing opportunities for students to explain their creative choices, reflect on how their ideas developed, and describe the role AI played in the process. These practices shift attention away from whether AI was used and toward how students engaged with it and what they contributed to the final work.

Creativity, Pedagogy, and Purpose

The central question is not simply, "How can students use AI creatively?" A more useful starting point is, "What kind of creative thinking do we want students to practice?" The answer may involve exploring possibilities, solving problems, making connections, taking risks, revising ideas, or expressing a personal perspective.

Once that purpose is clear, educators can decide whether generative AI has a meaningful role. It may help students explore alternatives, challenge an initial idea, or see a problem from another perspective. In other situations, the learning may be better served by asking students to develop ideas without AI assistance. The decision should follow the learning purpose rather than the capabilities of the technology.

Creativity in the age of AI does not require choosing between human creativity and generative AI. It requires intentional decisions about how technology fits into the creative process while students remain active thinkers, decision-makers, and creators. As throughout this series, the guiding principle remains the same: pedagogy comes first, and technology should serve to extend, not replace, good teaching.

Looking Ahead

With Creativity in the Age of AI, we have explored the four teaching and learning areas introduced at the beginning of this series: assessment, feedback, collaboration, and creativity. Across each topic, generative AI has introduced new possibilities as well as new questions, but the starting point has remained consistent. Effective integration begins with what we want students to learn and experience, not with what the technology can do. As teaching continues to evolve alongside generative AI, that pedagogy-first perspective will continue to guide the conversations ahead.

References

Chiu, M. C., & Hwang, G. J. (2026). Enhancing student creative and critical thinking in generative AI-empowered creation: a mind-mapping approach. Interactive Learning Environments, 34(2), 869–890. https://doi.org/10.1080/10494820.2025.2511244

Duong, L.H. (2026). Preparing future creators: how media students navigate the role of AI in creative expression. Psychological Science and Education, 31(3), 181–195. https://doi.org/10.17759/pse.2026310313

Habib, S., Vogel, T., Anli, X., & Thorne, E. (2024). How does generative artificial intelligence impact student creativity? Journal of Creativity, 34(1), 100072. https://doi.org/10.1016/j.yjoc.2023.100072

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Washington, G. (2026, August 31). Creativity in the Age of AI [Blog post]. Retrieved from https://pedagogybeforetechnology.blogspot.com/

Image created by the author using OpenAI's ChatGPT, 2026.

Collaboration in the Age of AI

In Feedback in the Age of AI, we explored how feedback shapes learning through reflection, revision, and dialogue. That discussion leads naturally to collaboration. Feedback often occurs through interaction between educators and students and among peers. Collaboration extends that interaction, creating opportunities for shared thinking, problem-solving, and meaning-making. As generative AI becomes part of the learning environment, educators face a new question: How can collaboration remain meaningful and authentic when students can also collaborate with AI?

Why Collaboration Matters

Collaboration has long been central to teaching and learning. Through group work, discussion, and shared inquiry, students learn to articulate ideas, consider alternative perspectives, and build knowledge together. These processes support not only content understanding but also communication, critical thinking, and interpersonal skills.

Generative AI introduces a new dimension to collaboration. Students can now use AI to brainstorm ideas, draft responses, or explore alternative approaches independently. While these tools can support learning, they also raise important questions. If students turn first to AI rather than to one another, how does that shift the role of peer interaction? And how can educators design collaborative experiences that continue to prioritize human engagement and shared learning?

Collaboration as a Learning Process

Effective collaboration is not simply dividing tasks among group members. It is a process of shared thinking, negotiation, and reflection. Students benefit from opportunities to explain their reasoning, question assumptions, and build on one another’s ideas. Research on collaborative learning shows that collaboration itself is essential for deeper understanding and knowledge construction (Bach & Thiel, 2024).

In the age of AI, maintaining this process is essential. AI can contribute ideas quickly, but it does not replace the learning that occurs when students grapple with uncertainty together. Educators play a key role in designing collaborative activities that require interaction, dialogue, and decision-making, which are elements that cannot be fully outsourced to AI.

 Collaborative Learning with AI

When used thoughtfully, AI can support collaboration rather than replace it. For example, students might use AI as a starting point for discussion, generating initial ideas that they then evaluate, refine, or challenge as a group. In this way, AI becomes one voice among many, rather than the dominant source of input.

At the same time, collaboration in the age of AI requires clear expectations. Students benefit from understanding when AI use is appropriate, how it should be documented, and how it fits within collaborative work. Framing AI as a tool to support group thinking, not as a substitute for it, helps maintain the integrity of collaborative learning.

Examples of Collaboration in the Age of AI

Across these examples, the goal is not to prevent AI use, but to design collaboration in ways that keep students interacting with one another. AI can support the process, but it should not replace the dialogue that makes collaboration meaningful.

AI-Supported Brainstorming:

Groups may use AI to generate initial ideas or perspectives on a topic, then evaluate those ideas together. Students can discuss which suggestions are useful, which are incomplete, and how they might build on them. This encourages critical thinking and shared decision-making.

Collaborative Problem-Solving:

In problem-based tasks, students can compare their own approaches with AI-generated solutions. The group can analyze differences, identify strengths and limitations, and justify their chosen approach. This shifts the focus from finding an answer to understanding the reasoning behind it.

Group Reflection and Synthesis:

After completing a collaborative task, students can use AI to summarize key points or identify themes, then refine or challenge that summary as a group. This reinforces collective understanding while ensuring that students remain actively engaged in shaping the outcome.

Collaboration, Accountability, and Transparency

One of the challenges of collaboration, especially in group work, is ensuring accountability. Generative AI adds another layer to this challenge, as it can be difficult to distinguish between individual and shared contributions.

Clear expectations help address this. Educators can ask students to document their process, describe how AI was used, or reflect on their contributions to group work. Studies of AI use in education highlight the importance of transparency and clear guidelines in maintaining trust and accountability (Kasneci et al., 2023).

Transparency also supports trust. When students understand how AI fits into collaborative tasks, they are more likely to use it responsibly and thoughtfully.

Collaboration, Pedagogy, and Purpose

In the age of AI, collaboration remains a powerful way to support learning, but its purpose must be clear. When collaborative activities are designed with intention, they encourage students to engage with one another, develop shared understanding, and practice skills that extend beyond individual performance.

Generative AI can be part of this process, but it should be guided by pedagogy. By designing collaborative experiences that emphasize dialogue, reasoning, and reflection, educators can ensure that collaboration continues to support meaningful learning.

 Looking Ahead

Collaboration and creativity are closely connected. Collaborative environments often provide the space for new ideas to emerge, develop, and take shape. In the next article, Creativity in the Age of AI, we will explore how generative AI influences creative thinking and how educators can support originality and expression when AI tools are part of the learning process.

References

Bach, A., & Thiel, F. (2024). Collaborative online learning in higher education—quality of digital interaction and associations with individual and group-related factors. Frontiers in Education, 9, 1356271. https://doi.org/10.3389/feduc.2024.1356271

Kasneci, E., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274

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Washington, G. (2026, April 30). Feedback in the Age of AI [Blog post]. Retrieved from https://pedagogybeforetechnology.blogspot.com/

Image generated by ChatGPT

Feedback in the Age of AI

In Assessment in the Age of AI, we examined how formative and summative assessment can remain authentic and aligned with learning goals as generative AI becomes part of teaching and learning. Closely tied to assessment is feedback: assessment signals what we value, and feedback shapes how students respond to that signal. As students gain access to instant, AI-generated comments and suggestions, educators face a new question: How do we ensure feedback remains personal, instructive, and connected to learning goals rather than reduced to automated responses?

Why Feedback Matters

Feedback is central to learning because it helps students understand where they are in relation to learning goals and what steps they can take next. Well-designed feedback is timely, specific, and actionable; above all, it invites students to revise and reflect. Generative AI makes it easier than ever to produce quick comments, model revisions, or suggested edits. That speed can increase opportunities for practice and self-monitoring, but it also risks encouraging students to attend only to surface fixes rather than deeper reasoning. Recent work on classroom formative assessment and AI emphasizes that feedback is most effective when it supports reflection and self-regulated learning rather than simply providing answers (Hopfenbeck et al., 2023).

Feedback as Part of the Learning Process

Effective feedback is not a one-way transmission of information. It is embedded in cycles of practice, revision, and reflection. When educators design opportunities for students to act on feedback by revising drafts, solving new problems, or explaining their reasoning, feedback becomes a path for growth. In the age of AI, educators still set the conditions that make feedback productive: clear criteria, opportunities for action, and prompts that encourage students to explain their thinking.

AI can extend these cycles by producing examples, highlighting patterns in student work, or offering immediate suggestions that students can test. But those affordances have pedagogical value only when students are taught to interpret and evaluate machine-generated suggestions. Educators can scaffold this by asking students to annotate the feedback they accept or reject, to compare AI-generated suggestions with peer comments, or to write brief reflections on how feedback shaped a revision.

Formative Feedback and AI

Formative feedback supports learning as it unfolds. It is often low-stakes and focused on growth rather than evaluation, making it a natural space to consider thoughtful use of generative AI.

When guided by clear learning goals, AI can support formative feedback in ways that extend learning opportunities rather than replace instructor input. For example, students might use AI to receive initial feedback on clarity, organization, or completeness before submitting work. This can help them identify areas for improvement early and arrive at instructor feedback better prepared to engage with it.

At the same time, formative feedback in the age of AI requires careful framing. Students benefit from understanding the limits of AI-generated feedback and from evaluating suggestions critically. Instructors can encourage this by asking students to reflect on how feedback, whether from AI or an instructor, shaped their revisions or by prompting them to justify the changes they chose to make.

Summative Feedback and AI

Feedback also plays a role in summative assessment, even when grades are involved. Summative feedback helps students understand their performance, carry learning forward into future courses, and reflect on their growth over time.

Using AI for summative feedback presents both opportunities and challenges. AI can assist instructors by helping organize comments or identify common patterns in student work. However, summative feedback risks becoming impersonal if over-automated. Students still value feedback that reflects an instructor’s judgment, perspective, and understanding of their work (Alghamdi & Alghizzi, 2025).

Meaningful summative feedback emphasizes reasoning, decision-making, and application. Rather than focusing solely on what was “wrong,” it helps students understand how their thinking aligns with expectations and how they might approach similar tasks differently in the future.

Feedback and Human Connection

One of the lasting strengths of feedback is its human connection. Feedback communicates care, attention, and belief in a student’s ability to improve. These qualities are central to students’ motivation and engagement and are not easily replicated by generative AI.

As educators integrate AI into feedback practices, maintaining this human connection becomes even more important. Clear communication about when and how AI may be used, along with opportunities for dialogue such as conferences, peer review, or reflective activities, helps ensure that feedback remains relational and instructional.

Feedback, Pedagogy, and Purpose

In the age of AI, feedback choices should reflect what educators want students to learn and how they want them to learn it. When feedback is guided by pedagogy, AI can support timely responses and extended practice while educators focus on interpretation, judgment, and encouragement.

Rather than replacing feedback, generative AI invites educators to be more intentional about its role. By designing feedback practices that emphasize reflection, agency, and growth, educators can ensure that feedback remains a powerful tool for learning—one that reinforces the central message of this series: pedagogy comes first, and technology should serve to extend, not replace, good teaching.

Looking Ahead

Feedback and collaboration are closely connected. Feedback often occurs through interaction between educators and students, and among peers. In the next article, Collaboration in the Age of AI, we will explore how generative AI intersects with group work, shared problem-solving, and learning communities, and how collaboration can remain meaningful and authentic when AI is part of the learning environment.

References

Alghamdi, L. H., & Alghizzi, T. M. (2025). Educators’ reflections on AI-automated feedback in higher education: Potentials, pitfalls, and ethical dimensions. Frontiers in Education, 10, 1704820. https://doi.org/10.3389/feduc.2025.1704820

Hopfenbeck, T. N., Zhang, Z., Sun, S. Z., Robertson, P., & McGrane, J. A. (2023). Challenges and opportunities for classroom-based formative assessment and AI. Frontiers in Education, 8, 1270700. https://doi.org/10.3389/feduc.2023.1270700

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Washington, G. (2026, January 31). Feedback in the Age of AI [Blog post]. Retrieved from https://pedagogybeforetechnology.blogspot.com/

Image generated by ChatGPT 

Assessment in the Age of AI

In Teaching in the Age of AI, we argued that meaningful integration of generative AI must begin with pedagogy rather than technology. That principle becomes especially important when we turn to assessment. Assessment shapes what students prioritize, how they allocate effort, and how they define success. As generative AI changes how students draft, revise, and generate work, long-standing assessment practices are being tested in new ways. Rather than responding with restriction or uncertainty, this moment invites educators to revisit the purpose of assessment itself and consider how both formative and summative practices can remain meaningful, aligned with learning goals, and guided by pedagogy when AI is part of the teaching and learning environment.

Why Assessment First?

Tensions around generative AI often surface first in assessment. Questions about academic integrity, originality, and fairness often arise in graded work, even as AI influences how students prepare, practice, and learn. Because assessment signals what is valued in a course, it plays a powerful role in shaping student behavior. Beginning this series with assessment allows educators to examine how grading, feedback, and evaluation can better reflect what we truly want students to learn. These goals include critical thinking, application, reflection, and growth, even as we acknowledge the realities of AI-supported learning.

Revisiting Formative and Summative Assessment

To understand how assessment can evolve in the age of AI, it is helpful to revisit the distinction between formative and summative assessment. While both play important roles, they serve different purposes in the learning process.

Formative assessment is designed to support learning as it unfolds. It includes activities such as drafts, practice problems, reflections, and low-stakes checks for understanding. In the context of generative AI, formative assessment offers opportunities to help students practice skills, receive feedback, and reflect on their thinking without the pressure of grades. When aligned with clear learning goals, formative assessment can encourage students to use AI as a learning support rather than a shortcut.

Summative assessment, by contrast, is intended to evaluate learning at a particular point in time. Exams, final projects, presentations, and major papers often carry higher stakes and greater consequences. As generative AI becomes more capable, summative assessments are where many educators feel the greatest uncertainty. This uncertainty invites a deeper question: not simply how to prevent inappropriate AI use, but how to design summative assessments that emphasize application, reasoning, and decision-making. These are outcomes that remain closely tied to human judgment.

Formative Assessment in the Age of AI

Formative assessment is often described as assessment for learning rather than assessment of learning. Its purpose is to support students as they develop understanding, practice skills, and make sense of new ideas. For these reasons, formative assessment is a natural place for thoughtful integration of generative AI. Because formative activities are typically low stakes, they offer space for experimentation, feedback, and reflection, key elements of learning that can be strengthened, rather than undermined, by AI.

When aligned with clear learning goals, formative assessment can help students use AI as a learning support rather than a shortcut. For example, AI can assist students in generating practice questions, revising drafts, or testing their understanding of concepts before submitting work for evaluation. The emphasis remains on the learning process: making thinking visible, identifying misconceptions, and refining ideas over time.

Examples of formative assessment with AI include:

• Drafting and Revision: Students may use AI to receive feedback on early drafts of writing assignments. Rather than submitting AI-generated text as final work, students can ask for suggestions related to clarity, organization, or argument development, then decide which revisions to apply. This positions AI as a feedback partner while keeping students responsible for their thinking and choices.
• Reflection and Metacognition: AI can support reflective prompts that encourage students to explain their reasoning, compare approaches, or articulate what they found challenging. For instance, students might be asked to reflect on how AI-assisted feedback influenced their revision process or what they learned by comparing their initial response with an AI-generated one.

In each of these examples, the value of AI lies not in automating learning but in extending opportunities for practice and reflection. The educator’s role remains central: setting expectations, framing appropriate use, and helping students interpret feedback meaningfully. When formative assessment is designed with intention, generative AI can support deeper engagement with learning goals while reinforcing the principle that pedagogy, and not technology, guides assessment decisions.

Summative Assessment in the Age of AI

Summative assessment serves a different purpose than formative assessment. Rather than supporting learning as it unfolds, summative assessment is designed to evaluate learning at a particular point in time. Final exams, major projects, presentations, and portfolios typically carry higher stakes and have a significant impact on course outcomes.

In the age of AI, the challenge of summative assessment is not simply determining whether AI was used but ensuring that assessments are aligned with what educators truly want students to demonstrate. When summative tasks focus primarily on producing information, AI assistance can blur the line between student understanding and generated output. This moment invites educators to revisit the design of summative assessments and consider how they can emphasize application, reasoning, decision-making, and reflection. These are outcomes that remain closely tied to human judgment.

Examples of summative assessment approaches in the age of AI include:

• Applied and Contextualized Tasks: Rather than asking students to reproduce content, summative assessments can require them to apply concepts to specific contexts, cases, or problems. Students might analyze a scenario, make decisions based on evidence, or justify choices using course concepts. These tasks focus on thinking and reasoning, even if AI is used as a support during the process.
• Process-Oriented Projects: Summative assessments can incorporate checkpoints or reflective components that ask students to document how their work developed over time. Students may be asked to explain their approach, describe challenges they encountered, or reflect on how feedback (human or AI-assisted) influenced their final product. This shifts attention from the final artifact alone to the learning process behind it.

Across these examples, the goal is not to eliminate AI from summative assessment, but to design assessments that make learning visible. Clear expectations about appropriate AI use, paired with tasks that emphasize judgment and interpretation, help ensure that summative assessment remains meaningful and fair. When summative assessments are thoughtfully designed, generative AI becomes a consideration in the learning environment rather than a threat to assessment integrity.

Assessment, Pedagogy, and Purpose

Whether formative or summative, assessment sends powerful signals about what is valued in learning. In the age of generative AI, those signals matter more than ever. When assessment is guided by pedagogy, educators can design experiences that emphasize thinking, reflection, and application rather than production alone. Formative assessment offers space for practice and feedback, while summative assessment provides opportunities for students to demonstrate understanding in meaningful and authentic ways.

Revisiting assessment through a pedagogy-first lens does not require abandoning long-standing practices, but it does invite intentional design. As generative AI becomes part of the learning environment, assessment choices should reflect what educators want students to learn, how they want them to learn it, and how understanding can be made visible. In this way, assessment remains a tool for learning rather than a barrier, reinforcing the central message of this series: pedagogy comes first, and technology should serve to extend, not replace, good teaching.

Looking Ahead
Assessment and feedback are closely connected. While assessment signals what is valued in learning, feedback shapes how students respond to that signal as they revise, reflect, and move forward. As generative AI becomes part of the assessment landscape, questions about feedback—its timing, purpose, and impact—become increasingly important. In the next article, Feedback in the Age of AI, we will explore how educators can design feedback practices that remain meaningful, personal, and supportive of learning, even when AI tools are involved.

References

Hopfenbeck, T. N., Zhang, Z., Sun, S. Z., Robertson, P., & McGrane, J. A. (2023). Challenges and opportunities for classroom-based formative assessment and AI: a perspective article. Frontiers. https://doi.org/10.3389/feduc.2023.1270700

Ilieva, G., Yankova, T., Ruseva, M., & Kabaivanov, S. (2025). A framework for generative AI-Driven assessment in higher education. Information, 16(6), 472. https://doi.org/10.3390/info16060472

Xia, Q., Weng, X., Ouyang, F. et al. (2024). A scoping review on how generative artificial intelligence transforms assessment in higher education. International Journal of Educational Technology in Higher Education, 21(40). https://doi.org/10.1186/s41239-024-00468-z

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Washington, G. (2025, December 31). Assessment in the Age of AI [Blog post]. Retrieved from https://pedagogybeforetechnology.blogspot.com/

Teaching in the Age of AI

 

Generative AI has quickly become part of the teaching landscape. Educators are using it to prepare lesson materials, design assessments, generate examples, and create practice activities that reinforce student learning. At the same time, many are asking how these tools might influence their professional judgment and their relationships with students. Like earlier shifts in educational technology, AI is prompting educators to reflect on what aspects of their practice are essential and what might be strengthened through new tools.

History offers perspective. Each wave of technology, from calculators to online platforms, has raised concerns and created opportunities. Over time, these tools found their place in classrooms when teachers discovered how to use them to support learning rather than distract from it. Generative AI is the latest of these shifts. The question for educators today is not whether AI belongs in teaching, but how to design instruction that uses it purposefully while keeping pedagogy first.
 

Looking Back
In our earlier Generative AI Meets Pedagogy series, we examined how AI can connect with three teaching frameworks: Universal Design for Learning, Active Learning, and Experiential Learning. Each article highlighted ways that AI might extend existing approaches when guided by clear pedagogy. This mirrors what researchers are now finding. A recent review of generative AI in K–12 settings concluded that the most promising uses occur when educators align AI tools with specific instructional goals and receive adequate preparation to use them effectively (Marzano, 2025). When aligned with strong pedagogy, AI can support personalized learning, strengthen student motivation, and provide timely feedback.

In higher education, similar themes are emerging. Universities worldwide are experimenting with generative AI in teaching and learning, but challenges such as academic integrity and assessment design remain pressing. A recent study argues that the key question for universities is not whether AI will transform teaching, but how educators will reshape learning experiences to emphasize authenticity, critical thinking, and meaningful engagement (Yusuf, Pervin, & Román-González, 2024). Across K–12 and higher education, the message is clear: AI is most valuable when it is grounded in pedagogy rather than driving it.

Pedagogy First, Still
Teaching has always been more than the tools available. It is about relationships, curiosity, and the thoughtful design of learning experiences. Generative AI cannot replace those human elements, but it can create opportunities when used with purpose. Without direction, AI risks encouraging work that lacks depth or originality, or overreliance on quick answers. With careful integration, however, it can give educators more flexibility, support feedback, and allow more time for meaningful interaction with students.

Rather than asking what AI can do, the more important question is what educators want students to learn and experience. That clarity allows them to decide where AI might add value and where it should be limited. Keeping pedagogy first ensures that creativity, collaboration, and critical thinking remain at the heart of education. In this way, AI becomes not a replacement for teaching, but a support that allows educators to focus on what matters most: guiding students toward deeper understanding and meaningful learning.

What’s Ahead in This Series
With this foundation in mind, Teaching in the Age of AI will focus on the core practices that shape everyday instruction. Rather than centering on tools, each article will examine a key element of teaching and consider how it can be strengthened when AI is present. The goal is not to replace what educators already do well, but to highlight ways AI can be used thoughtfully to support teaching and learning.

Articles for this blog are published four times a year at the end of January, April, August, and December. Following today’s launch article, the series will continue with Assessment in the Age of AI in December 2025, Feedback in the Age of AI in January 2026, Collaboration in the Age of AI in April 2026, and Creativity in the Age of AI in August 2026. Each article will return to a single principle: pedagogy comes first, and technology should serve as a tool to extend, not replace, good teaching.

Looking Ahead
We will begin this series with Assessment in the Age of AI. Assessment has always shaped how students approach learning—what they prioritize, how they spend their time, and what they value. With AI reshaping how students complete tasks, receive feedback, and create projects, assessment is the right place to start reimagining teaching in this new era. In the next article, we will consider how both formative and summative assessments can remain authentic, fair, and aligned with pedagogy when AI is part of the classroom.

References
Marzano, D. (2025). Generative artificial intelligence (GAI) in teaching and learning processes at the K-12 level: A systematic review. Technology, Knowledge and Learning. https://doi.org/10.1007/s10758-025-09853-7

Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21(21). https://doi.org/10.1186/s41239-024-00453-6

Generative AI Meets Experiential Learning

Our previous exploration examined how generative AI can enhance active learning by fostering engagement and critical thinking. Now, let's delve into its role in experiential learning, where students gain knowledge through direct experiences and reflection. Educators can create dynamic, reflective, and authentic learning experiences that prepare students for real-world challenges by integrating Generative AI (GenAI) into experiential learning. In this article, we will examine how generative AI can be integrated into each phase of Kolb’s Experiential Learning Cycle to bring new depth, adaptability, and responsiveness to experiential education (Salinas-Navarro et al., 2024).

What Is Experiential Learning?

Experiential learning is an educational approach where learners acquire knowledge and skills through direct experiences, followed by reflection and conceptualization. This method emphasizes active participation, allowing students to engage deeply with the subject matter.
Kolb, a prominent educational theorist, formalized this concept in his Experiential Learning Theory. According to Kolb (1984, 2014), effective learning occurs when a learner progresses through a four-stage cycle:

1. Concrete Experience: Engaging in a new experience or encountering a familiar situation in a novel way.
2. Reflective Observation: Thoughtfully reviewing and reflecting on the experience.
3. Abstract Conceptualization: Formulating theories or models based on reflections.
4. Active Experimentation: Applying the new ideas to the world around them to see what results.

This cyclical process allows learners to transform experiences into knowledge, fostering deeper understanding and the ability to apply learning in various contexts.

Experiential learning can take many forms, including internships, service-learning projects, simulations, and fieldwork. By immersing students in real-world challenges, this approach bridges the gap between theoretical knowledge and practical application, preparing learners for complex problem-solving and decision-making in their future careers.

Generative AI Integration

With the advent of Generative AI (GenAI), educators have new opportunities to enrich each stage of this cycle. By thoughtfully integrating GenAI tools, we can create more immersive experiences, facilitate deeper reflection, support conceptual understanding, and encourage innovative experimentation. This section explores how GenAI can be effectively embedded within each phase of Kolb's model to enhance experiential learning outcomes.

1. Concrete Experience: Engaging in Authentic Scenarios

In this initial stage, learners immerse themselves in new experiences, forming the foundation for subsequent reflection and learning. GenAI can facilitate this by generating realistic simulations and scenarios tailored to specific learning objectives. For instance, AI-driven role-playing exercises can place students in complex situations—such as managing a business crisis or conducting a scientific experiment—allowing them to engage actively and make decisions in a risk-free environment. These AI-generated experiences provide diverse contexts, enhancing learners' exposure to varied situations.

2. Reflective Observation: Facilitating Deep Reflection

Following the experience, learners reflect on their actions and outcomes to derive insights. GenAI tools can support this reflective process by prompting learners with thought-provoking questions like, "What challenges did you encounter?" or "How did your decisions impact the outcome?" Additionally, AI can assist in summarizing key events from the experience, helping learners to identify patterns and areas for improvement. This structured reflection fosters critical thinking and self-awareness.

3. Abstract Conceptualization: Developing Theoretical Understanding

In this phase, learners integrate their reflections to form abstract concepts and generalizations. GenAI can aid by connecting learners' experiences to relevant theories and frameworks, offering explanations and resources that deepen understanding. For example, after reflecting on a group project, AI can introduce concepts of team dynamics or leadership theories that relate to the observed outcomes. This integration bridges practical experiences with academic knowledge, solidifying learning.

4. Active Experimentation: Applying Knowledge in New Contexts

The final stage involves applying newly acquired knowledge to different situations, testing hypotheses, and observing results. GenAI can propose new scenarios or modify existing ones, allowing learners to apply their conceptual understanding in varied contexts. For instance, after learning about conflict resolution strategies, AI can simulate a new workplace dispute for the student to navigate, reinforcing and testing their skills. This iterative process promotes adaptability and continuous learning.

For guidance on writing effective prompts, refer to the Best Practices for Writing Effective Prompts.

Conclusion

Incorporating generative AI into Kolb’s Experiential Learning Cycle not only enriches each stage but also fosters a dynamic, iterative learning process. By leveraging GenAI tools, learners can engage in authentic experiences, receive immediate feedback, and apply newfound knowledge in varied contexts. This integration promotes deeper understanding, adaptability, and continuous improvement, aligning with the principles of authentic assessment and preparing students for real-world challenges.

References:

Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice-Hall.

Kolb, D. A. (2014). Experiential learning: Experience as the source of learning and development (2nd ed.). Pearson Education.

Salinas-Navarro, D. E., Vilalta-Perdomo, E., Michel-Villarreal, R., & Montesinos, L. (2024). Designing experiential learning activities with Generative Artificial Intelligence tools for authentic assessment. Interactive Technology and Smart Education, 21(4), 708–734. https://doi.org/10.1108/itse-12-2023-0236
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Washington, G. (2025, April 30). Generative AI Meets Experiential Learning [Blog post]. Retrieved from https://pedagogybeforetechnology.blogspot.com/
 

Generative AI Meets Active Learning

In our ongoing exploration of how generative AI intersects with pedagogical frameworks, we've previously discussed its alignment with Universal Design for Learning (UDL). Now, let's delve into how generative AI can enhance active learning—a dynamic approach that emphasizes student engagement through activities like problem-solving, discussion, and application of concepts. By thoughtfully integrating AI tools into active learning environments, educators can create more responsive, personalized, and effective learning experiences that cater to diverse student needs.

 What is Active Learning?

Active learning is a student-centered pedagogical approach that shifts the focus from passive reception of content to meaningful participation in the learning process. Rather than simply listening to lectures or memorizing facts, students are engaged in higher-order thinking activities such as analyzing, synthesizing, evaluating, and applying information through collaborative discussions, peer teaching, and problem-solving exercises. These activities foster deeper understanding and retention of knowledge by placing learners in the role of active constructors rather than passive consumers of information.

Incorporating active learning strategies into teaching not only promotes critical thinking and metacognitive skills but also increases student satisfaction and motivation (ElSayary, 2024). By being actively involved in the learning process, students are better equipped to understand complex concepts, make connections across disciplines, and apply knowledge in real-world contexts. These benefits are foundational for developing 21st-century competencies such as adaptability, collaboration, and lifelong learning—qualities that are increasingly essential in today’s dynamic educational landscape.

As education evolves in response to digital innovation, integrating emerging technologies like generative AI offers new opportunities to enhance active learning practices. The following section explores how generative AI can be thoughtfully integrated in active learning environments to deepen student engagement, support metacognitive regulation, and foster personalized learning.

 Integrating Generative AI into Active Learning

Generative AI tools, such as ChatGPT and Claude, can be powerful allies in promoting Active Learning. Their capabilities extend beyond mere information retrieval, offering dynamic interactions that foster deeper engagement and critical thinking among students.

​Enhancing Student Engagement and Critical Thinking

Generative AI tools can simulate Socratic dialogue, prompting students to think critically and articulate their understanding. These tools facilitate interactive learning experiences by enabling students to explore concepts through dialogue, question formulation, and iterative feedback. This approach aligns with active learning principles, where students construct knowledge through inquiry and reflection.

​Supporting Personalized and Adaptive Learning

These AI tools can tailor learning experiences to individual student needs. By analyzing student inputs, they can adjust the complexity and focus of information presented, thereby supporting differentiated instruction. This adaptability ensures that students remain challenged yet not overwhelmed, promoting sustained engagement and learning efficacy.​

 

Facilitating Collaborative Learning Environments

Generative AI can serve as a collaborative partner in group settings, assisting in brainstorming sessions or providing diverse perspectives on a topic. Their ability to process and generate information rapidly allows students to explore multiple facets of a subject, fostering a more comprehensive understanding through peer discussions and collaborative projects.

​Encouraging Metacognition and Self-Regulated Learning

By interacting with AI tools, students are prompted to reflect on their thought processes and learning strategies. This metacognitive engagement helps students become more aware of their cognitive processes, enabling them to regulate their learning more effectively. Such self-regulation is a key component of active learning, as it empowers students to take ownership of their educational journey.​

Incorporating generative AI tools like ChatGPT and Claude into educational practices offers a multifaceted approach to active learning, enhancing student engagement, personalization, collaboration, and self-regulation. Educators should thoughtfully integrate these technologies to complement traditional teaching methods, ensuring that the human element remains central to the learning experience.

Practical Applications

Implementing generative AI in active learning can take various forms, each enhancing student engagement and understanding:

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• Case Studies and Simulations: AI can create realistic scenarios for students to analyze, promoting the application of theoretical knowledge.

Example Prompt: "Generate a case study involving a company facing ethical dilemmas in data privacy. Include background information, stakeholder perspectives, and potential consequences."​

• Immediate Feedback: AI-driven platforms can offer instant feedback on assignments, enabling students to reflect and improve in real-time.

Example Prompt: "Review the following essay on climate change for coherence, grammar, and argument strength. Provide constructive feedback and suggestions for improvement."​

• Interactive Tutorials: AI can guide students through complex problems step-by-step, adapting explanations to their learning pace.

Example Prompt: "Provide a detailed explanation of the photosynthetic process, emphasizing the molecular mechanisms of light-dependent and light-independent reactions."​

These applications not only enhance engagement but also foster a deeper understanding of the subject matter, aligning with the core principles of active learning.

Considerations for Implementation

While integrating AI into active learning environments offers significant potential, it's crucial to approach this integration thoughtfully.​

Maintain Human Oversight

Educators should actively guide generative AI interactions to ensure they align with learning outcomes and provide contextually appropriate content. Generative AI tools can support instruction by generating ideas or facilitating personalized learning paths, but they should not replace the educator's role in fostering critical thinking and contextual understanding. Establishing clear guidelines on AI usage helps maintain academic integrity and ensures that technology serves as an aid rather than a substitute for human instruction.

​Promote Critical Thinking

Encouraging students to question and evaluate AI-generated content fosters analytical skills and deeper engagement with the material. By critically assessing the accuracy and relevance of AI outputs, students develop the ability to recognize credible information and become more autonomous learners. Incorporating activities that require students to compare AI-generated responses with traditional research can enhance their evaluative skills and understanding of subject matter.

Ensure Accessibility

Selecting generative AI tools that are accessible to all students is essential to promote equity in the learning environment. Consideration should be given to diverse needs and backgrounds, including varying levels of digital literacy and access to technology. Providing training sessions, alternative resources, and support mechanisms ensures that all students can effectively engage with AI-enhanced learning activities. Additionally, choosing generative AI platforms that comply with accessibility standards helps accommodate students with disabilities, fostering an inclusive educational experience.

For guidance on writing effective prompts, refer to the Best Practices for Writing Effective Prompts.

Looking Ahead

As we continue to explore the synergy between generative AI and pedagogical strategies, our next focus will be on experiential learning. We'll examine how generative AI can create immersive experiences that bridge the gap between theory and practice.​

Stay tuned for the next article in our series: Generative AI Meets Experiential Learning.

Reference:

 

ElSayary, A. (2024). Integrating generative AI in active learning environments: Enhancing metacognition and technological skills. Journal of Systemics, Cybernetics and Informatics, 22(3), 34–37. https://doi.org/10.54808/jsci.22.03.34

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Washington, G. (2025, January 31). Generative AI Meets Active Learning [Blog post]. Retrieved from https://pedagogybeforetechnology.blogspot.com/