Undergraduate Students’ Use of AI Tools to Support Interdisciplinary Learning

The rapid expansion of AI-driven technologies has begun to reshape how undergraduate students engage in interdisciplinary learning across domains. This study examines how students integrate AI tools into their cross-disciplinary academic practices. Based on 16 semi-structured interviews with students enrolled in interdisciplinary programs, we explored the perceived benefits, challenges, and evolving learning strategies that emerge as AI becomes embedded in their academic routines.  Students described using AI to clarify unfamiliar disciplinary concepts, compare perspectives across fields, and generate preliminary interpretations that help them enter new knowledge domains with greater confidence. At the same time, the data reveal growing tensions in students’ sense-making processes. Many participants expressed uncertainty about the accuracy and epistemic authority of AI outputs, and they reported struggling to determine when AI functions as a productive guide and when it becomes a constraint on deeper inquiry. Students also negotiated concerns about academic integrity, especially when AI assistance blurred the boundary between support and substitution.

Using reflexive thematic analysis informed by Braun and Clarke’s framework, we identified three themes. Students described AI as a cognitive bridge that helped them translate concepts across disciplines. They also engaged in AI-supported epistemic experimentation, using AI to compare disciplinary perspectives and generate alternative interpretations. At the same time, they navigated concerns about trust, autonomy, and academic integrity, particularly regarding over-reliance on AI and uncertainty about institutional expectations.

The findings reveal that AI tools are not merely efficiency enhancers but function as epistemic intermediaries that shape how students conceptualize and integrate knowledge across domains. This study contributes to emerging discussions on AI-assisted qualitative inquiry by illustrating how learners’ sense-making processes intersect with algorithmic mediation. Implications for instructional design, interdisciplinary pedagogy, and the responsible integration of AI in higher education are discussed.