A Student-AI Collaborative Approach to Reduce Cognitive Offloading in IS Education

Lauren De Guzman, Timothy R. Hill, Yu Chen

Generative AI has rapidly become embedded in universities, offering students immediate assistance across academic coursework. While these tools provide efficiency, accessibility, and convenience, they also raise growing concerns that students may increasingly rely on AI to generate the initial layer of thought, positioning them as selectors of AI-generated ideas rather than as active thinkers developing their own reasoning. As this reliance grows, students may hand off the mental processes of forming initial interpretations and engaging in analytical reasoning to AI, contributing to cognitive offloading and reduced learning engagement (Pallant et al., 2025). Although many generative AI systems include features designed to guide users through structured questions, these processes are often optional, while direct AI-generated responses remain the more attractive and commonly used option. Addressing concerns about cognitive offloading requires rethinking how students interact with generative AI during the problem-solving process. Building on this idea, we propose a student-AI collaborative approach that encourages students to actively participate in shaping solutions rather than relying solely on AI-generated responses. Instead of immediately generating a completed answer, the system first asks students to contribute their own initial interpretations, reasoning, or problem analysis. AI-guided questioning then serves as a structured follow-up process, encouraging students to clarify, expand, and challenge their reasoning as the interaction develops. As a result, students are encouraged to move beyond repeated AI-driven responses and engage more thoughtfully with their own problem-solving and learning. We tested this approach in an undergraduate MIS class, and the preliminary results were promising. Helpfulness ratings remained comparable to those from direct AI-generated responses, but student feedback reflected appreciation for having a more active role in shaping their own solutions. The findings suggest that collaborative AI-supported problem-solving can remain useful to students while also encouraging greater engagement and critical thinking throughout the learning process. As generative AI becomes more prevalent in higher education, simply limiting student use might not be sufficient to address the risks of cognitive offloading. Meanwhile, if universities simply allow or encourage the use of AI, students may prioritize outcomes over processes and thus become less engaged in their own cognitive development. Encouraging students to engage with AI as a critical dialogue partner, which challenges their questions, assumptions, and reasoning rather than simply producing answers, offers a more intentional direction for supporting deeper learning and critical thinking in the classroom.