How can generative AI support student brainstorming without replacing thinking, voice, or agency? This episode highlights an open-access article about a GenAI-enabled brainstorming app used in an interdisciplinary course.
See our full episode notes at https://www.centerforengagedlearning.org/student-engagement-with-genai-brainstorming/.
How can generative AI support student brainstorming without replacing thinking, voice, or agency? This episode highlights an open-access article about a GenAI-enabled brainstorming app used in an interdisciplinary writing course:
Chia, Joanne, and Angela Frattarola. 2025. “A Design-Based Approach to Analysing Student Engagement with a GenAI-Enabled Brainstorming App.” Computers and Education: Artificial Intelligence 9. https://doi-org.elon.idm.oclc.org/10.1016/j.caeai.2025.100468
This episode was hosted, edited, and produced by Jessie L. Moore, Director of the Center for Engaged Learning and Professor of Professional Writing & Rhetoric.
60-Second SoTL is produced by the Center for Engaged Learning at Elon University.
Music: “Cryptic” by AudioCoffee.
Image in episode art was created by ChatGPT 5.2, using the prompt, "Please create a landscape-orientation image depicting what 'Nudgy,' the GenAI tool described in this article, might look like." Feedback prompted ChatGPT to "Please revise to reduce the text on the screen, and the center Nudgy and his speech bubble on the computer screen."
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Jessie L. Moore:
How can generative AI support student brainstorming without replacing thinking, voice, or agency? That’s the focus of this week’s 60-second SoTL from Elon University’s Center for Engaged Learning. I’m Jessie Moore.
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In “A design-based approach to analysing student engagement with a GenAI-Enabled brainstorming app,” Joanne Chia and Angela Frattarola examine student engagement with Waai [why] (spelled w-a-a-i), a GenAI-enabled brainstorming app created for a first-year interdisciplinary writing course at Nanyang Technological University in Singapore. Their open-access article appears in Computers & Education: Artificial Intelligence.
Unlike many AI writing assistants, Waai was intentionally designed not to generate text for students. Instead, it supports the early, often messy stages of writing—brainstorming, ideation, and sense-making—through a unidirectional chatbot aptly named Nudgy. Waii interfaced with ChatGpT-4.
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Central to Waai’s design is the idea that writing is a form of thinking. Nudgy’s prompts are tied directly to assignment stages without allowing free prompting or text generation.
This controlled prompt engineering was intentional. The goal was to:
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Waai was piloted across two semesters with approximately 130 undergraduate students enrolled in a required writing module.
To understand student engagement with the app, the researchers collected multiple forms of data:
Qualitative data from surveys and focus groups were analyzed inductively using a discovery-oriented coding approach, with multiple researchers independently reviewing themes to strengthen credibility.
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Across data sources, students reported five primary benefits of using the Nudgy chatbot:
First, students appreciated the pre-engineered prompts, especially because they mirrored the language and expectations of course rubrics. Many felt this made Waai more pedagogically relevant than tools like ChatGPT, used without the Waai interface.
Second, students consistently valued that Nudgy suggested topics to explore rather than providing answers. This distinction mattered deeply. Students described the chatbot as “probing” or “triggering” ideas while preserving ownership of their writing and human creativity.
Third, students found Nudgy especially helpful for broadening perspectives. Prompts that asked them to consider how people with different disciplinary perspectives—or even a Martian—might approach their topic helped students rethink assumptions and identify new angles for inquiry.
Fourth, Nudgy helped model the process of ideation itself. For students who struggled to find concepts or connections, seeing how the chatbot explained why certain ideas might matter gave them a template for thinking they could apply independently.
Finally, students valued Nudgy’s constant availability, noting that it reduced their reliance on instructors and supported late-night or asynchronous work without judgment.
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At the same time, students identified several important limitations.
Many struggled to determine which AI-generated ideas were most relevant, describing the feedback as expansive but difficult to “drill down.” Nudgy was seen as effective for expansion but less helpful for concentration or synthesis—especially when integrating multiple sources into an argument.
Students also expressed mistrust of GenAI, particularly around correctness, academic integrity, and what they called “Aigiarism.” Even when they valued AI for brainstorming, many worried that overuse could weaken creativity or dilute personal voice.
Crucially, students recognized—and often accepted—that GenAI cannot provide personal judgment or lived experience. These, they argued, had to remain human responsibilities.
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Chia and Frattarola argue that Waai demonstrates a productive middle ground for GenAI in higher education: AI as a thinking companion, not a content generator. Their findings challenge narratives of AI as either a shortcut or a threat, instead positioning GenAI as pedagogically valuable when embedded in thoughtful instructional sequencing and human-AI collaboration.
For SoTL scholars, this study contributes:
Ultimately, the authors suggest that GenAI’s greatest educational value may lie not in efficiency or accuracy, but in its ability to provoke reflection, uncertainty, and self-regulation—all essential conditions for deep learning.
To learn more about this study, visit our show notes for a link to the open access article.
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Jessie L. Moore:
Join us for our next episode of 60-second SoTL from Elon University’s Center for Engaged Learning for another snapshot of recent scholarship of teaching and learning. Learn more about the Center at www.CenterForEngagedLearning.org.
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