Impact of Artificial Intelligence on the Roles of Mathematics Teachers: A Systematic Literature Review

The Future of Mathematical Literacy in The Era of Artificial Intelligence: A Systematic Literature Review

It is proposed that mathematical literacy in the AI era should extend beyond traditional competencies to include the ability to critically evaluate AI-generated outputs, identify algorithmic limitations, and use AI responsibly a construct this review tentatively terms Mathematical AI Literacy.

A Systematic Literature Review of Artificial Intelligence (AI) and Academic Performance in Higher Education

The findings indicate that AI positively influences academic performance primarily through enhanced engagement, personalization, predictive analytics, and self-efficacy.

Evaluating the impact of artificial intelligence in mathematics education: A systematic literature review

Artificial Intelligence (AI) as a representation of technological advancement, has been recognized for its potential to positively and negatively impact the educational sphere. In mathematics education, AI revolutionizes learning by offering students more flexible and adaptive learning experiences. This study aims to provide insights into the impact of AI integration in mathematics education through a systematic literature review (SLR). The primary focus of this research includes trends in AI usage based on year, country, and education level, types of AI integration, and the impacts and challenges encountered in implementing AI in mathematics learning. The research data comprises empirical articles collected using the PRISMA approach, drawing from databases such as Scopus, ERIC, and SAGE over the past five years. This results in the analysis of 13 selected articles. The findings reveal dynamic trends in AI utilization, particularly in Indonesia and Australia. In Indonesia, AI is primarily used to expand access to online education, while in Australia, it is leveraged for personalized learning. AI is predominantly integrated at the middle school and university levels, with ChatGPT as the primary tool to assist students in understanding mathematical concepts. The benefits of AI include improvements in cognitive, affective, and teaching abilities. The challenges of AI identified include low user awareness, limited educator skills, cognitive impacts on students, and technological access disparities that exacerbate educational inequalities.

Artificial Intelligence in Primary Mathematics Education: A Systematic Review of Pedagogical Applications, Benefits, and Challenges

Effectiveness of AI-based tutoring and assessment systems in mathematics education: a systematic review

Artificial intelligence (AI) is increasingly transforming mathematics education through adaptive tutoring systems, automated feedback, and AI-supported assessment tools. However, evidence regarding the effectiveness of these technologies remains dispersed across diverse contexts and study designs. This systematic review synthesised empirical research on the effectiveness of AI-based tutoring and assessment systems in mathematics education published between 2015 and 2025. Guided by the PRISMA framework, a comprehensive search was conducted in Scopus and Web of Science using database-specific search strings. After screening 1,749 records and assessing 76 full-text articles for eligibility, 12 studies met the inclusion criteria and were included in the final synthesis. Data were extracted using a structured framework and study quality was appraised using the Mixed Methods Appraisal Tool (MMAT). Due to heterogeneity in interventions, populations, and outcome measures, a narrative synthesis was employed. Findings indicate that AI-based tutoring systems and adaptive learning platforms generally support improvements in mathematics achievement, particularly among lower-performing learners, although effectiveness varies depending on implementation fidelity, learner characteristics, and instructional context. Studies focusing on generative AI tools such as ChatGPT primarily reported positive perceptions and increased engagement, but evidence of direct achievement gains remains limited. Overall, the evidence base demonstrates moderate methodological quality, with stronger conclusions drawn from experimental and quasi-experimental designs. The review highlights the potential of AI-based tutoring and assessment systems to enhance mathematics learning while emphasising the need for rigorous, large-scale experimental research and clearer reporting of intervention mechanisms.

Mapping the landscape of undergraduate artificial intelligence use in higher education: a scoping review

As artificial intelligence (AI) tools become increasingly accessible, their adoption by undergraduate students has outpaced institutional understanding of how, why, and with what consequences students use them. This scoping review maps the nature, extent, and gaps in the peer-reviewed evidence on undergraduates’ direct use of AI in higher education. A systematic search of Scopus, Web of Science, and MEDLINE identified 35 studies published between 2022 and 2024. Data were charted across four research questions, namely patterns of AI use, factors influencing adoption, educational outcomes, and emergent concerns. The evidence base is concentrated post-2022, dominated by cross-sectional surveys with single-institution convenience samples, and geographically weighted towards non-Western contexts. Within these studies, undergraduate AI use centres on academic task support, language learning, and supplemental instruction, with students consistently valuing efficiency and accessibility. However, significant areas of contestation emerge, including unresolved questions about whether AI enhances or undermines critical thinking, inconsistent demographic influences on adoption, and heterogeneous findings from the Technology Acceptance Model. Concerns about inaccuracy, academic integrity, diminished human interaction, and uneven AI literacy persist across contexts. Several priority gaps are identified, including the absence of longitudinal designs, adequately powered experimental studies on cognitive effects, intersectional demographic analyses, research on speech-based and multimodal AI tools, student perspectives on institutional AI policies, and intervention studies testing AI literacy curricula. This review provides a structured evidence map and research agenda for a rapidly evolving field, offering a direction of research on undergraduate AI engagement in higher education.