Generative Artificial Intelligence in Higher Education: Implications for Academic Performance, Critical Thinking, and Students’ Mental Well-Being
Keywords:
Generative Artificial Intelligence, Higher Education, Academic Performance, Critical Thinking, Mental Well-Being, AI Literacy, Academic Integrity, Digital LearningAbstract
Generative Artificial Intelligence (GenAI) has rapidly become a prominent component of higher education, with tools such as ChatGPT, Gemini, and Copilot increasingly used for information retrieval, brainstorming, writing support, problem solving, presentations, coding, and examination preparation. While these technologies can provide timely and personalised academic assistance, their increasing use also raises concerns about overdependence, academic integrity, reduced cognitive effort, misinformation, and students’ psychological well-being. The present conceptual paper examines the educational implications of GenAI across three interrelated domains: academic performance, critical thinking, and mental well-being. Drawing on the existing literature and the issues identified in the original paper, the study adopts an analytical review approach to synthesise potential benefits and risks of AI-assisted learning. The review indicates that GenAI can improve learning efficiency, accessibility, language support, and personalised learning when students actively evaluate and use AI-generated information. Conversely, uncritical dependence may encourage cognitive offloading, weaken independent problem solving, reduce originality, and increase uncertainty regarding academic and career competencies. AI use may also have mixed psychological consequences: it can reduce workload and provide accessible learning support, but excessive screen-based engagement, dependency, academic pressure, and uncertainty about future employment may contribute to stress and digital fatigue. The paper argues for a human-centred model of AI integration in which GenAI functions as a learning assistant rather than a substitute for students’ intellectual effort or teachers’ pedagogical role. The paper concludes with recommendations for AI literacy, source verification, redesigned assessment, ethical guidelines, and psychologically supportive educational practices.