Determinants and Impact of AI-Based Chatbot Adoption in Higher Education: A TAM-ISSM Perspective
SAGE OPEN, cilt.16, sa.4, 2026 (SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 4
- Basım Tarihi: 2026
- Doi Numarası: 10.1177/21582440261495170
- Dergi Adı: SAGE OPEN
- Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, EBSCO Education Source, Education Abstracts, ERIC (Education Resources Information Center), Directory of Open Access Journals, Academic Search Ultimate (EBSCO), Social Science Premium Collection (ProQuest), Education Source Ultimate (EBSCO)
- Hatay Mustafa Kemal Üniversitesi Adresli: Hayır
Özet
Generative artificial intelligence (GenAI) has the potential to transform higher education by enabling data-driven learning design and evidence-based educational decision-making. Among GenAI applications, chatbots provide real-time feedback, content support, and personalized guidance, thereby reshaping the way students engage with learning environments. While the use of AI-based chatbots by students is becoming increasingly widespread, empirical evidence on their adoption and impact in higher education remains limited. This study investigates the determinants of GenAI-based chatbot adoption and its perceived educational impact among university students, integrating the Technology Acceptance Model and the Information System Success Model. Drawing on survey data from 303 university students collected through online questionnaires and analysed via PLS-SEM, the findings highlight the critical role of perceived ease of use, perceived usefulness, information quality, system quality, and instructor attitude in shaping satisfaction and continued usage. Furthermore, results demonstrate that GenAI-based chatbot usage is perceived by students to significantly enhance their academic performance, learning motivation, and productivity. Beyond adoption dynamics, this study underscores how AI-based chatbots can contribute to the future of AI-supported learning design by clarifying which system and user level factors drive engagement and effectiveness. In particular, the unique focus on students' perceptions of instructor attitude provides new insights for curriculum modifications, showing how perceived faculty endorsement legitimizes AI use and contributes to more inclusive and effective technology integration. By connecting adoption, satisfaction, and individual learning outcomes, this research contributes to ongoing debates on how GenAI tools can support evidence-based educational decision-making and the design of learning environments.