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/

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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