
This research explores the integration of AI chatbots in higher education, examining their impact on teaching practices, learning outcomes and student engagement. AI's ability to predict and adapt offers transformative potential for educational environments, shifting skill demands and enhancing accessibility. Using the Technological Pedagogical Content Knowledge (TPACK) framework, we assess the integration of AI tools by educators. At the same time, the Technology Acceptance Model (TAM) gauges teacher’s perceptions and Constructivist Learning Theory (CLT) explores AI's support for interactive, student-centered learning. Our developed framework includes the roles and responsibilities of human educators and AI chatbots, strategies for maximizing educational benefits and addressing challenges and best practices for fostering human-AI partnerships and promoting inclusivity. The findings reveal that AI chatbots can significantly reduce administrative burdens on educators, offer personalized learning experiences, and enhance real-time feedback. This study underscores the importance of teacher training and ethical considerations in AI integration. Ultimately, we aim to contribute to the effective integration of AI in education, promoting a balanced synergy between human and technological efforts to enhance educational practices.
Conversational agent chatbots, Constructivist Learning Theory (CLT), Technological Pedagogical Content Knowledge (TPACK), Human-AI partnerships, AI chatbots, Technology Acceptance Model (TAM), Artificial intelligence (AI), Student-centered learning
Conversational agent chatbots, Constructivist Learning Theory (CLT), Technological Pedagogical Content Knowledge (TPACK), Human-AI partnerships, AI chatbots, Technology Acceptance Model (TAM), Artificial intelligence (AI), Student-centered learning
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 7 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
