Agentic Generative AI in Higher Education: Perceived Benefits, Risks, and Implications for Learning

Generative artificial intelligence (GAI) is increasingly used in higher education for explanation, content generation, feedback, and personalized learning. However, conventional prompt–response chatbots may provide insufficient support for complex academic tasks requiring planning, evidence organization, comparison, justification, verification, and reflection. This study proposes the Agentic GAI-Supported Learning Framework for Higher Education (AGAI-HE), which positions GAI agents as bounded, human-supervised learning partners rather than substitutes for students or instructors. Following an instructor-led demonstration, a convenience sample of 130 students rated three approaches applied to the same e-commerce task: traditional e-learning without GAI, standard prompt–response GAI-chatbot support, and a prompt-based simulation of the AGAI-HE workflow. Students observed rather than individually executed the three approaches; no autonomous agent, persistent agent environment, API-based workflow, or integrated multi-agent system was deployed. Both AI-supported approaches received higher ratings than traditional e-learning on an overall perceived-support score, with large pairwise effects and a parallel descriptive pattern across four strongly overlapping facets. No statistically significant difference was found between the GAI chatbot and the AGAI-HE simulation. The findings constitute an exploratory, perception-based evaluation and do not validate the framework’s educational effectiveness, demonstrate an additional advantage of agentic support, or establish improved learning performance. The evaluation was limited to student perceptions; instructor acceptance, implementation feasibility, workload, and educational effectiveness were not assessed.