ChatGPT and other AIs: Personal relief and limitations among mathematics-oriented learners
Vol 9, Issue 1, 2024, Article identifier:
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Abstract
Mathematics utilizes symbolic language, logics, relationships, and numerical connections that made it challenging for students to learn and develop their computational skills. Recently, artificial intelligence emerged as a supplement tool for education and learning because of its ability to detect relationships and logics. Academic institutions are looking on how to implement AI-assisted learning specifically for mathematics to aid in increasing the quantitative competence of students. This study analyzed the relief and limitations of AI chat models in learning among mathematics-oriented students in higher education. Fifteen students participated in this study from mathematics-oriented courses e.g., engineering, statistics, and education. Interviews were conducted on how students used AI chat models to assist in learning mathematical concepts and methods. Narratives indicated that AI chat models like ChatGPT and Bard were capable of accurately responding to chat prompts in problem solving, proving, and explanations. It was prominent that these AI models understood the mathematical language and use of symbols for integration, derivatives, limit, fractions, exponentials, and intervals. At some instances, AI models could give inaccurate results or incorrect methods for solving; they also sometimes give correct answers on second run of chat prompts after these mistakes. These results had promising implications in education as these accessible AI models could reinforce the firsthand learning of mathematical foundations. This preliminary study offered important usability of AI models in mathematics in assisting students and monitoring their learning progress.
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1. Qiu Y, Pan J, Ishak NA. Effectiveness of artificial intelligence (AI) in improving pupils’ deep learning in primary school mathematics teaching in Fujian province. Computational Intelligence and Neuroscience 2022; 2022: 1–10. doi: 10.1155/2022/1362996
2. Barroso C, Ganley CM, McGraw AL, et al. A meta-analysis of the relation between math anxiety and math achievement. Psychological Bulletin 2021; 147(2): 134–168. doi: 10.1037/bul0000307
3. Sherman BF, Wither DP. Mathematics anxiety and mathematics achievement. Mathematics Education Research Journal 2003; 15(2): 138–150. doi: 10.1007/bf03217375
4. Duhaylungsod AV, Chavez JV. ChatGPT and other AI users: Innovative and creative utilitarian value and mindset shift. Journal of Namibian Studies 2023; 33: 4367–4378. doi: 10.59670/jns.v33i.2791
5. Bishop CM. Pattern Recognition and Machine Learning. Springer; 2006.
6. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521(7553): 436–444. doi: 10.1038/nature14539
7. MacKay DJ. Information Theory, Inference and Learning Algorithms. Cambridge university press; 2003.
8. Raghu M, Schmidt E. A survey of deep learning for scientific discovery. arXiv 2020; arXiv:2003.11755. doi: 10.48550/arXiv.2003.11755
9. Wagner AZ. Constructions in combinatorics via neural networks. arXiv 2021; arXiv:2104.14516. doi: 10.48550/arXiv.2104.14516
10. Peifer D, Stillman M, Halpern-Leistner D. Learning selection strategies in Buchberger’s algorithm. arXiv 2020; arXiv:2005.01917. doi: 10.48550/arXiv.2005.01917
11. Lample G, Charton F. Deep learning for symbolic mathematics. arXiv 2019; arXiv:1912.01412. doi: 10.48550/arXiv.1912.01412
12. He YH. Machine-learning mathematical structures. International Journal of Data Science in the Mathematical Sciences 2023; 1(1): 23–47. doi: 10.1142/S2810939222500010
13. Chavez JV, Lamorinas DD, Ceneciro CC. Message patterns of online gender-based humor, discriminatory practices, biases, stereotyping, and disempowering tools through discourse analysis. Forum for Linguistic Studies 2023; 5(2): 1535. doi: 10.59400.fls.v5i2.1535
14. The World Bank. In Ecuador, artificial intelligence makes learning math easier. Available online: https://www.worldbank.org/en/news/feature/2022/02/10/en-ecuador-aprender-matematicas-es-mas-facil-con-inteligencia-artificial-nivelacion-remediacion-academica (accessed on 10 July 2023).
15. Moore K. Using ChatGPT in math lesson planning. Available online: https://www.edutopia.org/article/using-chatgpt-plan-high-school-math-lessons/ (accessed on 10 July 2023).
16. Thorp HH. ChatGPT is fun, but not an author. Science 2023; 379(6630): 313–313. doi: 10.1126/science.adg7879
17. Baidoo-Anu D, Owusu Ansah L. Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI 2023; 7(1): 52–62.
18. Firat M. What ChatGPT means for universities: Perceptions of scholars and students. Journal of Applied Learning and Teaching 2023; 6(1): 1–22. doi: 10.37074/jalt.2023.6.1.22
19. Ouyang F, Jiao P. Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence 2021; 2: 100020. doi: 10.1016/j.caeai.2021.100020
20. Greeno JG, Collins AM, Resnick LB. Cognition and learning. In: Handbook of Educational Psychology. Routledge; 1996.
21. Schommer M. Effects of beliefs about the nature of knowledge on comprehension. Journal of Educational Psychology 1990; 82(3): 498–504. doi: 10.1037/0022-0663.82.3.498
22. Bandura A. Social Foundations of Thought and Action. Prentice Hall; 1985.
23. Liu CH, Matthews R. Vygotsky’s philosophy: Constructivism and its criticisms examined. International Education Journal 2005; 6(3): 386–399.
24. Bandura A. Toward a psychology of human agency. Perspectives on Psychological Science 2006; 1(2): 164–180. doi: 10.1111/j.1745-6916.2006.00011.x
25. Riedl MO. Human-centered artificial intelligence and machine learning. Human Behavior and Emerging Technologies 2019; 1(1): 33–36. doi: 10.1002/hbe2.117
26. Shneiderman B. Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction 2020; 36(6): 495–504. doi: 10.1080/10447318.2020.1741118
27. Deeva G, Bogdanova D, Serral E, et al. A review of automated feedback systems for learners: Classification framework, challenges and opportunities. Computers & Education 2021; 162: 104094. doi: 10.1016/j.compedu.2020.104094
28. Hwang GJ, Xie H, Wah BW, Gašević D. Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence 2020; 1: 100001. doi: 10.1016/j.caeai.2020.100001
29. Kawakami A, Guerdan L, Cheng Y, et al. Towards a learner-centered explainable AI: Lessons from the learning sciences. arXiv 2022; arXiv:2212.05588. doi: 10.48550/arXiv.2212.05588
30. Lee D, Huh Y, Lin CY, Reigeluth CM. Technology functions for personalized learning in learner-centered schools. Educational Technology Research and Development 2018; 66: 1269–1302. doi: 10.1007/s11423-018-9615-9
31. Singh K. Quantitative Social Research Methods. SAGE; 2007. doi: 10.4135/9789351507741
32. Chavez JV. Narratives of bilingual parents on the real-life use of English language: Materials for English language teaching curriculum. Arab World English Journal 2022; 13(3): 325–338. doi: 10.24093/awej/vol13no3.21
33. Calaro MF, Vicente MB, Chavez JV, et al. Marketing campaigns leading to the purchase of accommodation products: A content analysis. Journal of Namibian Studies 2023; 33: 4221–4236. doi: 10.59670/jns.v33i.2696
34. Ceneciro CC, Estoque MR, Chavez JV. Analysis of debate skills to the learners’ confidence and anxiety in the use of the English language in academic engagements. Journal of Namibian Studies 2023; 33: 4544–4569. doi: 10.59670/jns.v33i.2812
35. Frost J. Purposive sampling: Definition & examples. Available online: https://statisticsbyjim.com/basics/purposive-sampling/ (accessed on 8 July 2023).
36. Esterberg KG. Qualitative Methods in Social Research. McGraw-Hill Humanities/Social Sciences/Languages; 2001.
37. Crosley J. What (exactly) is thematic analysis? Available online: https://gradcoach.com/what-is-thematic-analysis/ (accessed on 8 July 2023).
38. Duan P, Jia Y, Liang L, et al. Space-reserved cooperative caching in 5G heterogeneous networks for industrial IoT. IEEE Transactions on Industrial Informatics 2018; 14(6): 2715–2724. doi: 10.1109/TII.2018.2794615
39. Etcuban JO, Campanilla BS, Horteza AD. The use of Mathcad in the achievement of education students in teaching College Algebra in a university. International Electronic Journal of Mathematics Education 2019; 14(2): 341–351. doi: 10.29333/iejme/5718
40. Kilpatrick J. History of research in mathematics education. In: Encyclopedia of Mathematics Education. Springer, Dordrecht; 2014. pp. 349–354.
41. McCulloch AW, Hollebrands K, Lee H, et al. Factors that influence secondary mathematics teachers’ integration of technology in mathematics lessons. Computers & Education 2018; 123: 26–40. doi: 10.1016/j.compedu.2018.04.008
42. Swai CZ, Glanfield F. Teacher-led professional learning in Tanzania: Perspectives of mathematics teacher leaders. Global Education Review 2018; 5(3): 183–195.
43. Wardat Y, Tashtoush MA, AlAli R, Jarrah AM. ChatGPT: A revolutionary tool for teaching and learning mathematics. Eurasia Journal of Mathematics, Science and Technology Education 2023; 19(7): em2286. doi: 10.29333/ejmste/13272
44. Tack A, Piech C. The AI teacher test: Measuring the pedagogical ability of blender and GPT-3 in educational dialogues. arXiv 2022; arXiv:2205.07540. doi: 10.48550/arXiv.2205.07540
45. Cunningham-Nelson S, Baktashmotlagh M, Boles W. Visualizing student opinion through text analysis. IEEE Transactions on Education 2019; 62(4): 305–311. doi: 10.1109/TE.2019.2924385
46. Li L, Ma Z, Fan L, et al. ChatGPT in education: A discourse analysis of worries and concerns on social media. arXiv 2023; arXiv:2305.02201. doi: 10.48550/arXiv.2305.02201
47. Dyer E, Gur-Ari G. Minerva: Solving quantitative reasoning problems with language models. Available online: https://ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html (accessed on 9 July 2023).
48. Alobaidi OG, Crockett KA, O’Shea JD, Jarad TM. Abdullah: An intelligent arabic conversational tutoring system for modern islamic education. In: Proceedings of the World Congress on Engineering; 3–5 July 2013; London, UK.
49. Lauesen S. User Interface Design: A Software Engineering Perspective. Addison-Wesley; 2004.
50. Mageira K, Pittou D, Papasalouros A, et al. Educational AI Chatbots for content and language integrated learning. Applied Sciences 2022; 12(7): 3239. doi: 10.3390/app12073239
51. bin Mohamed MZ, Hidayat R, binti Suhaizi NN, et al. Artificial intelligence in mathematics education: A systematic literature review. International Electronic Journal of Mathematics Education 2022; 17(3): em0694. doi: 10.29333/iejme/12132
52. Wu R. RETRACTED: Visualization of basic mathematics teaching based on artificial intelligence. Journal of Physics: Conference Series 2021; 1992(4): 042042. doi: 10.1088/1742-6596/1992/4/042042
53. University of Cambridge. Mathematical paradox demonstrates the limits of AI. Available online: https://www.cam.ac.uk/research/news/mathematical-paradox-demonstrates-the-limits-of-ai (accessed on 9 July 2023).
54. Colbrook MJ, Antun V, Hansen AC. The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale’s 18th problem. Proceedings of the National Academy of Sciences 2021; 119(12): e2107151119. doi: 10.1073/pnas.2107151119
55. Chavez JV. Assessing online academic integrity and humanized teaching in Zamboanga Peninsula Polytechnic State University. Journal of Multidisciplinary in Social Sciences 2023; 19(1): 9–17.
56. Chavez J, Lamorinas DD. Reconfiguring assessment practices and strategies in online education during the pandemic. International Journal of Assessment Tools in Education 2023; 10(1): 160–174. doi: 10.21449/ijate.1094589
57. Chen Z. Artificial intelligence evaluation for mathematics teaching in colleges under the guidance of wireless network. Mobile Information Systems 2022; 2022; 3201004. doi: 10.1155/2022/3201004
DOI: https://doi.org/10.54517/esp.v9i1.1911
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