TY - JOUR
T1 - Artificial intelligence in mathematics education
T2 - The good, the bad, and the ugly
AU - Opesemowo, Oluwaseyi A.G.
AU - Ndlovu, Mdutshekelwa
N1 - Publisher Copyright:
© 2024, Duzce University, Faculty of Education. All rights reserved.
PY - 2024/9
Y1 - 2024/9
N2 - Integrating Artificial Intelligence [AI] into mathematics education offers promising advancements and potential pitfalls. Striking a balance between AI-driven developments and preserving core pedagogical principles is critical in the teaching and learning environment. AI has emerged as a transformative force in various fields, including education. In the realm of mathematics education, AI technologies offer a spectrum of potential benefits (including personalize instruction, adaptive assessment, interactive learning environments, and real-time feedback, among others) and challenges (such as lack of creativity and problem-solving skills, inability to explain reasoning, bias in data and algorithms, absence of emotional intelligence and data privacy and security concern etc). This conceptual study used autoethnography as the methodology and qualitative content approach to analyze data. The study discussed historical background of AI and considered ethical issues around AI. It was concluded that the journey to harness the full potential of AI in mathematics education requires careful navigation of the good, the bad, and the ugly aspects inherent in this technological evolution.
AB - Integrating Artificial Intelligence [AI] into mathematics education offers promising advancements and potential pitfalls. Striking a balance between AI-driven developments and preserving core pedagogical principles is critical in the teaching and learning environment. AI has emerged as a transformative force in various fields, including education. In the realm of mathematics education, AI technologies offer a spectrum of potential benefits (including personalize instruction, adaptive assessment, interactive learning environments, and real-time feedback, among others) and challenges (such as lack of creativity and problem-solving skills, inability to explain reasoning, bias in data and algorithms, absence of emotional intelligence and data privacy and security concern etc). This conceptual study used autoethnography as the methodology and qualitative content approach to analyze data. The study discussed historical background of AI and considered ethical issues around AI. It was concluded that the journey to harness the full potential of AI in mathematics education requires careful navigation of the good, the bad, and the ugly aspects inherent in this technological evolution.
KW - Artificial intelligence
KW - Big data
KW - ChatGPT
KW - Machine learning
KW - Mathematics education
UR - http://www.scopus.com/inward/record.url?scp=85201545867&partnerID=8YFLogxK
U2 - 10.33902/JPR.202426428
DO - 10.33902/JPR.202426428
M3 - Article
AN - SCOPUS:85201545867
SN - 2602-3717
VL - 8
SP - 333
EP - 346
JO - Journal of Pedagogical Research
JF - Journal of Pedagogical Research
IS - 3
ER -