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