Exploring University Students’ Interactions with Chatgpt in Education: An Examination of Usages Based on the UTAUT Model

Yıl 2025, Cilt: 5 Sayı: 2, 429 - 438, 29.10.2025

Ayda Sabuncuoğlu İnanç , Nesrin Akıncı Çötok , Aytekin İşman

Öz

Recent advancements in AI have led to educational chatbots like ChatGPT, enhancing student engagement through personalized, interactive experiences. These tools provide tailored assistance and immediate feedback, improving learning outcomes. The Unified Theory of Acceptance and Use of Technology (UTAUT) model identifies four key factors—PE, EE, SI, and FC —that influence technology adoption. In ChatGPT’s case, these factors affect students’ perceptions of the tool’s usefulness, ease of use, and available support. This study explores PE, EE, SI, and FC in ChatGPT use, focusing on differences between undergraduate and postgraduate students. 40 participants were interviewed using snowball sampling. Data were analyzed descriptively, with key statements quoted for insight. PE factors for undergraduates include task efficiency, convenience, academic productivity, and reliability issues, while postgraduates emphasized academic support, research applications, and reliability issues, with perceived usefulness and reliability common across both groups. EE factors for undergraduates include ease of use, prior experience, and educational assistance, while postgraduates emphasized prior experience, over-reliance, and self-reliance, with prior experience as the common theme. SIs for undergraduates are driven by “social pressure,” while postgraduates are influenced by “misuse anxiety” and “independent usage.” FC for both groups are centered on “accessibility,” focusing on free availability.

Anahtar Kelimeler

UTAUT , Chatgpt , AI Education , Undergraduate Students , Postgraduate Students

Kaynakça

• Jain, N. K., Bhaskar, K., & Jain, S. (2022). What drives adoption intention of electric vehicles in India? An integrated UTAUT model with environmental concerns, perceived risk, and government support. Research in Transportation Business & Management, 42, Article 100730. https://doi.org/10.1016/j.rtbm.2021.100730