Effectiveness of AI-Generated Study Materials versus Traditional Teaching Methods in Higher Education

Abstract

The increasing integration of artificial intelligence (AI) in higher education has significantly trans-formed the creation and dissemination of study materials. While AI-generated content offers advantages such as accessibility, scalability, and instant feedback, concerns remain regarding its depth, contex-tual relevance, academic integrity, and ability to support meaningful learning. This study examines student perceptions of AI-generated educational materials and explores faculty perspectives on AI-assisted evaluation, student dependence on AI-generated responses, and future assessment practices in higher education. A mixed-method research design was employed, involving 1,164 undergraduate students from diverse academic disciplines across private universities in India, along with structured interviews with faculty members from different age groups and younger AI experts. Quantitative data were collected using a structured 21-item Likert-scale questionnaire and analyzed using descriptive statistics, exploratory factor analysis (EFA), reliability analysis, and comparisons across academic years. Qualitative interview responses were thematically examined to identify concerns and proposed strategies related to AI-generated evaluation, academic integrity, student copying, and assessment redesign. The results indicate that students moderately acknowledge limitations of AI-generated materials, particu-larly regarding depth, contextual understanding, and human experiential insight. The EFA identifies a multidimensional structure explaining approximately 59% of the total variance, while the overall instru-ment demonstrates high internal consistency (Cronbach’s Alpha = 0.917). Furthermore, senior students demonstrate greater skepticism toward AI-generated materials than junior students. Faculty perspectives highlight a contrasting but complementary view: some participants favor limiting AI use and retaining subjective evaluation, whereas others emphasize adapting assessment practices to the inevitability of technological disruption through case-based, practical, analytical, and data-driven examinations. The study concludes that AI-generated materials are better positioned as supplementary learning resources than as standalone instructional tools, and that effective integration requires both human oversight and redesigned assessment strategies that emphasize independent reasoning and practical application.