Journal of Research in Science and Mathematics Education

Science Educators’ Perceived Challenges in Implementing Intelligent Tutoring Systems in Pedagogical Practices at Public Universities in Kogi State, Nigeria

2025-10-15T11:53:00+00:00

Purpose: This study explored the perceived challenges faced by science educators in implementing Intelligent Tutoring Systems (ITS) in pedagogical practices at public universities in Kogi State, Nigeria. Methodology: The study employed an exploratory approach using data from 52 science educators across four public universities. There was no sampling since the population was manageable. The study adopted a descriptive survey research design. An online Google form survey questionnaire titled Challenges of Implementing Intelligent Tutoring System Questionnaire (CIITSQ) was used for data collection. CIITSQ was trial tested, yielding a reliability value of 0.88 using Cronbach’s alpha. The CIITSQ contained 22 items. Two research questions and two null hypotheses guided the study. The research questions were answered using mean and standard deviation scores, while the null hypotheses were tested using t-test statistics. Findings: The study revealed inadequate technological infrastructure, financial constraints, lack of skilled personnel and training and ethical and social concerns as major barriers to the effective Implementation of ITS in pedagogical practices. The study also revealed that measures to address the challenges of implementing ITS in pedagogical practices involve a multifaceted approach, focusing on educators’ training, investment in technological infrastructure, curriculum development, institutional support, and addressing attitudinal and ethical concerns. Significance: The findings suggest that to harness the benefits of ITS in pedagogical practices successfully, a balanced approach is required, emphasizing strategic investments in robust AI-ITS and other ICT infrastructure, comprehensive training programs for educators, and the development of ethical guidelines and regulatory frameworks tailored to the local context.

Conceptual Rigor of AI-Generated Mathematical Explanations: The Case of Vector Functions

2025-12-07T05:40:05+00:00

Purpose: The rapid rise of generative artificial intelligence has reshaped discussions in mathematics education, particularly regarding the capacity of advanced systems such as ChatGPT and Gemini to support conceptual rigor. This study aims to investigate how these generative AI tools define and explain vector functions, including the procedures for differentiating and integrating them, in order to evaluate their conceptual rigor of ai-generated mathematical explanations and pedagogical potential. Methodology: Employing a qualitative case study design, the research analyzed responses generated by ChatGPT and Gemini to a structured mathematical prompt on vector functions. The explanations were compared with authoritative calculus textbooks using qualitative content analysis and a standardized scoring rubric. Findings: Findings reveal that both systems provide broadly accurate introductory descriptions of vector functions, highlighting their component-wise structure. However, notable gaps emerge in mathematical precision, particularly in specifying domains, ranges, and the formal conditions underlying differentiability and integrability. ChatGPT tends to include intuitive geometric interpretations, whereas Gemini provides concise procedural explanations, yet both models lack the rigorous logical framing found in standard mathematical texts. Despite these limitations, the systems demonstrate consistent procedural accuracy in describing differentiation and integration of vector-valued functions. Significance: The results underscore the educational potential of generative AI while highlighting the need for teachers to critically evaluate AI-generated mathematical content, particularly when these tools are used to support students’ conceptual learning in mathematics. These findings also highlight important implications for AI literacy, instructional design, and future research in mathematics education.