Expanding the lens: multi-institutional evidence on student use of ChatGPT in higher education

Abstract This study investigates how university students engage with generative artificial intelligence (GenAI), specifically ChatGPT, when completing knowledge-based academic tasks across six courses and two institutions. By comparing performance and perceptions in engineering and non-engineering subjects, the study examines whether students can use GenAI effectively without prior training and to what extent such tools meaningfully support learning. The work also explores how these findings may inform future research on accessible and inclusive learning design. A multi-method design was employed with 254 undergraduate and graduate students assigned to experimental groups (allowed to use ChatGPT) or control groups (restricted to traditional, non-GenAI resources). Quantitative analyses included descriptive statistics, a general linear model, and non-parametric comparisons, complemented by a topic-based analysis of open-ended survey responses addressing students’ perceptions, usage patterns, and desired functionalities. Students in the experimental groups generally obtained higher scores, with significant improvements in several subjects (e.g., computer systems administration, informatics, childhood disorders). A weak but significant positive correlation emerged between iterative engagement with ChatGPT (edits) and academic performance. Qualitative analysis showed that students valued ChatGPT for fast information access, clarification of concepts, and organizational support, while also expressing concerns about inaccuracies, overreliance, and limitations of free versions. GenAI can enhance student performance when used actively and reflectively, although its effectiveness varies by disciplinary context. The findings highlight the need for explicit AI-literacy instruction to ensure critical and responsible use. While the study does not directly address disability or accessibility outcomes, the qualitative patterns suggest potential intersections with inclusive and multimodal learning design, pointing to promising avenues for future research.