Developing a generative AI conceptual framework for higher education - Chukwuere, Joshua Ebere (The Indonesian Journal of Computer Science, 2025)
The overall aim of this research is to develop a whole-of-institution framework that supports the use of generative AI chatbots across higher education institutions (HEIs). The Generative AI Chatbots Acceptance Model (GAICAM) was developed from a blend of models that influence acceptance (TAM, UTAUT2, TPB, and more). optimism, innovativeness, discomfort, insecurity, and others; hence, including variables relevant to GAICAM. It is also critical to analyse and identify the implications for higher education of generative AI Chatbots. A research design was employed based on an integrative literature review and an active analysis of numerous studies from diverse databases, including IEEE, ACM, ScienceDirect, and Google Scholar. Part of the goal was to make sense of the implications for higher education created by AI Chatbots, which also necessitated identifying the prominent considerations relevant to implementation challenges and success. The search criteria were limited to peer-reviewed, English-language publications covering the use of AI chatbots in higher education that were published between 2020 and 2023. The findings demonstrate the degree to which AI chatbots have the capacity to drive improved student engagement, enhance the educational process, and support administrative and research tasks. But there are also clear difficulties, such as unfavorable student sentiments, doubts about the veracity of material produced by AI, and unease and nervousness with new technologies.