The findings suggest that the conscious and pedagogically grounded integration of AI into mathematics education holds considerable potential, however, the development of critical awareness and the continued presence of human oversight remain essential to ensure meaningful learning outcomes.
A ten-step teaching framework for AI-supported creative interactive content design is proposed, aimed at fostering pedagogical innovation while preserving critical thinking, creativity, and student authorship.
Belén Mainer, Ana Pérez-Escoda· Education sciences· 0 citations
ChatGPT has rapidly gained popularity among educators and learners, demonstrating its potential to facilitate interactive learning, personalized assistance, and academic support, and the need for guidelines, digital literacy, and ethical frameworks to ensure productive and balanced use of AI in education.
Anuradha Dwivedi, Pragati Pandey, Naveen Mni· International journal of com...· 0 citations
Empirical evidence is contributed from educational action research showing that structured pedagogical interventions can promote the critical, ethical, and responsible use of Generative Artificial Intelligence in education.
Rodrigo Florêncio da Silva· Information· 1 citation
The findings indicate that students integrate AI tools primarily as complementary learning aids rather than replacements for traditional materials, and highlights the growing importance of evaluating not only usage frequency but also perceived reliability and pedagogical value.
M. Altin, Kirsten Jäger, Silke Jütte et al.· IU Discussion Papers Busines...· 0 citations
Generative AI has an overall positive short-term impact on higher-level thinking skills, with the most significant improvements in problem-solving, moderate improvements in critical thinking, and relatively low improvements in creativity.
Fareen Wahid· International Journal of Inn...· 0 citations
This study investigates the application of generative artificial intelligence (generative AI) in mathematics education from 2022 to 2026. Drawing on a systematic literature review of 268 articles retrieved from OpenAlex, and EBSCO databases, we identify and analyze 66 core research articles through a rigorous screening process following PRISMA guidelines. The findings reveal three key patterns. First, generative AI applications in mathematics education span four domains: student learning support (64%), teacher instruction assistance (38%), assessment and feedback innovation (27%), and special education (2%), with student learning support as the dominant application area. Second, while generative AI shows positive effects in enhancing learning motivation and providing personalized support, it exhibits notable limitations in mathematical reasoning accuracy and critical thinking development—with 70% of studies documenting AI limitations. Third, challenges associated with these applications are multifaceted, encompassing technical (30%), pedagogical (52%), ethical (20%), and implementation (42%) dimensions. We propose an integrated analytical framework combining the Technological Pedagogical Content Knowledge (TPACK) framework with hybrid intelligence theory, and identify a novel pedagogical paradigm in which AI errors serve as instructional resources for cultivating critical thinking. Future research directions include broadening application domains, deepening theoretical frameworks, and strengthening empirical investigation. This review is limited to English-language publications from two databases, with the search closing in June 2026. Despite these limitations, the review contributes systematic evidence and practical guidance for the effective integration of generative AI in mathematics education.
Zeng-Fu Chao, Tianhong Han· Frontiers in Education· 0 citations
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