Machine learning reveals what makes university students keep using ChatGPT

Generative artificial intelligence has moved from novelty to near-ubiquity on university campuses in a remarkably short span of time, yet the question of whether students will actually keep using these tools for learning has remained surprisingly difficult to answer. A new study from the Faculty of Informatics at Eötvös Loránd University (ELTE) in Hungary set out to address precisely that question, asking what factors influence students’ decisions about whether they will continue to use ChatGPT for learning in the future. Rather than relying on the traditional statistical toolkit common in educational research, the team turned to an interpretable machine learning framework, a choice that allowed them to identify which factors most strongly shape the use of generative artificial intelligence in higher education while still keeping the results transparent and explainable.

The study, published in the Q1/D1-ranked journal Computers and Education: Artificial Intelligence published by Elsevier, examined the factors that influence students’ intention to continue using ChatGPT for academic purposes. The research was conducted by Chaman Verma, a member of the Department of Media and Educational Informatics at the Faculty of Informatics, as part of the University Excellence Scholarship Program (EKÖP-24). The work arrives at a moment when universities around the world are still wrestling with how to position generative AI within teaching, assessment, and academic integrity frameworks, making evidence about the drivers of sustained student use particularly timely.

At the heart of the investigation was a dataset built from questionnaire responses provided by 166 university students. Questionnaire-based studies of technology acceptance have a long pedigree in educational research, but the ELTE study distinguished itself through its analytical approach. Instead of applying only conventional regression techniques, the researcher employed interpretable machine learning methods to examine which factors play a decisive role in students’ intention to continue using ChatGPT in their learning. Interpretability is a crucial consideration here: machine learning models can be powerful at detecting patterns, but if a model behaves as a black box, its findings are of limited use to educators and policymakers who need to understand why certain factors matter, not just that they do.

The results of the analysis point to a cluster of key factors that determine whether students intend to keep ChatGPT in their academic lives. These include learning support, accessibility, academic engagement, usability, and the perceived reliability of the tool. In practical terms, the findings suggest that students are more likely to incorporate ChatGPT into their learning processes if they perceive it as genuinely useful, an instrument that helps them understand complex concepts, supports them in completing assignments, and contributes to more effective learning. Usefulness, in other words, is not an abstract judgment but a concrete experience of the tool helping with real academic tasks.

Each of the identified factors tells its own story about the student experience of generative AI. Learning support captures the degree to which ChatGPT functions as a study companion, something that can explain a difficult theorem, draft an outline, or translate dense material into more accessible language. Accessibility reflects how easily students can reach the tool when they need it, a consideration that has grown less restrictive as free and widely available versions of large language models have proliferated. Academic engagement speaks to whether interacting with the tool deepens or dilutes a student’s involvement with their coursework, while usability concerns the friction of the interaction itself, from the clarity of prompts to the quality of responses. Perceived reliability rounds out the picture, capturing how much students trust what the system tells them.

Beyond usefulness, the research highlights that trust is also key. The accuracy of responses, the reliability of the tool, and academic integrity remain central issues in the use of generative artificial intelligence in higher education. Students’ attitudes towards ChatGPT are influenced not only by how effectively the tool can support their learning, but also by how reliable they perceive it to be and how they view the possibilities for using it responsibly in an academic environment. This dual lens, usefulness on one side and trustworthiness on the other, mirrors a broader tension in the deployment of large language models: the same systems that can produce impressively fluent explanations can also generate confident but incorrect statements, a phenomenon that has become one of the most discussed challenges in applied artificial intelligence.

The academic integrity dimension adds a further layer of complexity. Universities have responded to the arrival of generative AI with a spectrum of policies, ranging from outright prohibition to structured integration into curricula, and students are acutely aware of where the boundaries lie. The ELTE findings suggest that a student’s willingness to continue using ChatGPT is bound up with their sense of whether such use is legitimate and responsible within their institution. A tool that students find helpful but ethically fraught may face very different adoption trajectories than one they perceive as both useful and sanctioned, and the interpretable machine learning approach allowed the study to weigh these considerations alongside more purely pragmatic factors.

Methodologically, the study contributes to a growing movement within educational data science that favors models whose reasoning can be inspected and communicated. Interpretable machine learning sits between the transparency of classical statistics and the predictive strength of more complex algorithms, offering researchers a way to capture nonlinear relationships and interactions among variables, for instance, how usability might amplify the effect of perceived usefulness, without sacrificing the ability to explain which inputs drove the predictions. For a question as policy-relevant as the determinants of continued AI use among students, that combination of predictive power and explainability is what makes the findings actionable rather than merely descriptive.

The study contributes new findings to research on the use of artificial intelligence in education and offers useful, evidence-based insights for educators, higher education institutions, and policymakers. As generative AI tools continue to evolve, understanding the conditions under which students choose to sustain their use, and the trust and integrity concerns that could temper it, will be essential for designing guidance, training, and institutional policy. The ELTE team’s work suggests that the path forward lies in ensuring that generative artificial intelligence tools can responsibly, consciously, and effectively support teaching and learning in higher education in the future, with usefulness and reliability advancing together rather than in competition.

For the wider conversation about AI in academia, the message from the 166 students behind this dataset is ultimately a measured one. They are prepared to embrace ChatGPT as a learning aid when it demonstrably helps them understand, engage, and perform, but their continued adoption depends on the tool earning and keeping their trust. As institutions refine their approaches to generative AI, studies of this kind, grounded in student data and analyzed with methods that make their conclusions visible, provide a template for evidence-based decision-making in a field where the technology is changing faster than the rules that govern it.

Subject of Research: Determinants of university students' continued use of ChatGPT for learning, analyzed with interpretable machine learning

Article Title: ChatGPT at university: What determines whether students will continue using it?

Article References: ChatGPT at university: What determines whether students will continue using it?. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: ChatGPT, generative artificial intelligence, higher education, interpretable machine learning, technology acceptance, learning support, academic integrity, perceived reliability, usability, student engagement, Eötvös Loránd University, educational data science