Artificial intelligence as a pedagogical companion for STEM research learning in higher education
Year: 2026
Type: article
Abstract: In many higher education contexts, particularly across Africa, the process of learning how to conduct research in science, technology, engineering, and mathematics (STEM) remains both cognitively demanding and structurally under-supported. Students often encounter limited supervision, delayed feedback, and minimal mentoring, conditions that restrict their growth as independent researchers. The widening gap between students and academic mentors has created frustration, anxiety, and disengagement. This paper rethinks STEM research learning in higher education by exploring the emerging role of artificial intelligence (AI) as a pedagogical companion rather than a replacement for human instruction. Drawing on peer-reviewed literature, the paper conceptually discusses how AI-powered tools can serve as companions in research-based learning, offering real-time support, cognitive scaffolding, writing assistance, and iterative feedback. Guided by Vygotsky’s Zone of Proximal Development and Cognitive Apprenticeship Theories, this paper examines how AI might enable students to access intellectual companionship in the absence of immediate supervision. The discussion spans issues of access, equity, ethical use, epistemological risks, and the socio-technical realities of AI integration in African higher education institutions. Key implications for policy, pedagogy, and capacity building are also discussed. The paper advocates a shift from rigid instructor-led paradigms to flexible, student-responsive research support ecosystems where AI technologies enhance, rather than replace, human pedagogical relationships. Ultimately, the paper positions AI not as a miracle solution, but as a complementary agent that can mediate the distance between curiosity and research mastery in STEM education.
Source: Discover Education
Author: Valentine Joseph Owan
Institution: University of Calabar
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $1,390
OpenAlex ID: W7169200901