Educational Neuroscience Meets AI: A Framework for Secondary Science Teaching
DOI:
https://doi.org/10.62695/RRUI1028Abstract
This study investigates the transformative potential of integrating artificial intelligence (AI) into secondary school science education from an educational neuroscience perspective. A literature review of studies published between 2013 and 2024 was conducted to identify key trends, challenges, and opportunities. The thematic analysis of selected sources informed the development of a practical framework that highlights applications and ethical considerations for educators. Findings indicate that AI can personalise learning, promote critical thinking, and enhance teacher-student interactions. However, successful implementation requires alignment with neuroscientific principles, ethical safeguards, and comprehensive teacher training. Challenges include data privacy concerns, algorithmic bias, and ensuring equitable access to AI technologies. The proposed framework offers actionable strategies for effectively integrating AI into science education, emphasising teacher preparedness, ethical practices, and ongoing evaluation to optimize AI’s impact on student learning. This novel framework bridges AI technology and educational neuroscience, providing valuable insights for educators and policymakers.
References
Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI Ethics, 2, 431–440. https://doi.org/10.1007/s43681-021-00096-7
Alabdulhadi, A., & Faisal, M. (2021). Systematic literature review of STEM self-study related ITSs. Education and Information Technologies, 26(2). https://doi.org/10.1007/s10639-020-10315-z
Albion, P. R., & Tondeur J. (2018). Information and communication technology and education: meaningful change through teacher agency. In J. Voogt, G. Knezek, R. Christensen, & K. W. Lai (Eds.), Handbook of information technology in primary and secondary education (pp. 1–15). Springer. https://doi.org/10.1007/978-3-319-71054-9_25
Amran, M. S., & Bakar, A. Y. A. (2020). We feel, therefore we memorize: Understanding emotions in learning mathematics using neuroscience research perspectives. Universal Journal of Educational Research, 8(11B), 5943–5950. DOI: 10.13189/ujer.2020.082229.
Blake, S., Choshanov, M., Paso, E., & Pape, S. (2003). Memory is not a warehouse, but a library. Using the achievements of neuropsychology in pedagogy in the United States. Teachers’ newspaper, 29. https://ug.ru/pamyat-ne-sklad-a-biblioteka-ispolzovanie-dostizhenij- nejropsihologii-v-pedagogike-ssha/
Bowers, J. S. (2016). The practical and principled problems with educational neuroscience. Psychological Review, 123(5). https://doi.org/10.1037/rev0000025
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77–101.
Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial Intelligence trends in education: A narrative overview. Procedia Computer Science, 136. https://doi.org/10.1016/j.procs.2018.08.233
Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in education: A review. IEEE Access, 8. https://doi.org/10.1109/ACCESS.2020.2988510
Chng, E., Tan, A. L., & Tan, S. C. (2023). Examining the use of emerging technologies in schools: A review of artificial intelligence and immersive technologies in STEM education. Journal for STEM Education Research, 6(3), 385–407. https://doi.org/10.1007/s41979-023-00092-y
Doroudi, S. (2023). The intertwined histories of artificial intelligence and education. International Journal of Artificial Intelligence in Education, 33(4), 885–928.
Drivas, T., & Doukakis, S. (2022, September). Introducing the fundamentals of Artificial Intelligence to K-12 classrooms according to educational neuroscience principles. In 2022 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) (pp. 1–7). IEEE.
Edeni, C. A., Adeleye, O. O., & Adeniyi, I. S. (2024). The role of AI-enhanced tools in overcoming socioeconomic barriers in education: A conceptual analysis. World Journal of Advanced Research and Reviews. 21(03), 944–951. https://doi.org/10.30574/wjarr.2024.21.3.0780
Fatimah, S., Sarwi, S., & Haryani, S. (2024). Artificial Intelligence in STEM education: A bibliometric analysis. International Journal of Multicultural and Multireligious Understanding, 11(1), 186–200. http://dx.doi.org/10.18415/ijmmu.v11i1.5273
Felder, R. M., & Brent, R. (2024). Teaching and learning STEM: A practical guide. John Wiley & Sons.
García-Martínez, I., Fernández-Batanero, J. M., Fernández-Cerero, J., & León, S. P. (2023). Analysing the impact of Artificial Intelligence and computational sciences on student performance: Systematic review and meta-analysis. Journal of New Approaches in Educational Research, 12(1). https://doi.org/10.7821/naer.2023.1.1240
Gentner, D. (2010). Psychology in cognitive science: 1978–2038. Topics in Cognitive Science, 2(3), 328–344. https://doi.org/10.1111/j.1756-8765.2010.01103.x
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-Inspired Artificial Intelligence. Neuron, 95(2). https://doi.org/10.1016/j.neuron.2017.06.011
Holstein, K., & Doroudi, S. (2022). Equity and artificial intelligence in education. In W. Holmes, & K. Porayska-Pomsta (Eds.), The Ethics of Artificial Intelligence in Education ((pp. 151–173). Routledge. DOI: 10.48550/arxiv.2104.12920.
Howard-Jones, P. A., Varma, S., Ansari, D., Butterworth, B., de Smedt, B., Goswami, U., Laurillard, D., & Thomas, M. S. C. (2016). The principles and practices of educational neuroscience: Comment on Bowers (2016). Psychological Review, 123(5). https://doi.org/10.1037/rev0000036
Howard-Jones, P., & Yau, S. (2018). Applying the science of learning in the classroom. Impact: Journal of the Chartered College of Teaching, 2, 9–12.
Huang, X., & Qiao, C. (2024). Enhancing computational thinking skills through artificial intelligence education at a STEAM high school. Science & Education, 33(2), 383–403. https://doi.org/10.1007/s11191-022-00392-6
Hwang, G. J., & Chang, H. F. (2011). A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students. Computers & Education, 56(4), 1023–1031. https://doi.org/10.1016/j.compedu.2010.12.002
Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in education. Computers and Education: Artificial Intelligence, 1, Article 100001. https://doi.org/10.1016/j.caeai.2020.100001
Immordino‐Yang, M. H., & Damasio, A. (2007). We feel, therefore we learn: The relevance of affective and social neuroscience to education. Mind, brain, and education, 1(1), 3–10. https://doi.org/10.1111/j.1751-228X.2007.00004.x
James, W. (1892). Text-book of psychology. Macmillan & Co. Ltd.
Kaswan, M. S., Chaudhary, R., Garza-Reyes, J. A., & Singh, A. (2024). A review of Industry 5.0: From key facets to a conceptual implementation framework. International Journal of Quality & Reliability Management. Advance online publication.
Kelly, A. M. C., Di Martino, A., Uddin, L. Q., Shehzad, Z., Gee, D. G., Reiss, P. T., & Milham, M. P. (2008). Development of anterior cingulate functional connectivity from late childhood to early adulthood. Cerebral Cortex, 19(3), 640–657. doi:10.1093/cercor/bhn117.
Kerr, I., & Earle, J. (2013). Prediction, preemption, presumption: How big data threatens big picture privacy. Stanford Law Review Online, 66, 65.
Krutka, D. G., Manca, S., Galvin, S. M., Greenhow, C., Koehler, M. J., & Askari, E. (2019). Teaching “against” social media: Confronting problems of profit in the curriculum. Teachers College Record, 121(14), 1–42. https://doi.org/10.1177/016146811912101410
Lenroot, R. K., & Giedd, J. N. (2006). Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging. Neuroscience & Biobehavioral Reviews, 30(6), 718–729. DOI: 10.1016/j.neubiorev.2006.06.001.
Li, L., Gow, A. D. I., & Zhou, J. (2020). The role of positive emotions in education: A neuroscience perspective. Mind, Brain, and Education, 14(3), 220–234. https://onlinelibrary.wiley.com/doi/10.1111/mbe.12244
Macpherson, T., Churchland, A., Sejnowski, T., DiCarlo, J., Kamitani, Y., Takahashi, H., & Hikida, T. (2021). Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research. Neural Networks, 144. https://doi.org/10.1016/j.neunet.2021.09.018
Martins, E. (2024). Appraisal of Artificial Intelligence and cost reduction management in educational institutions. SCIENCES, 3(1), 1–7.
Morrison, J., Frost, J., Gotch, C., McDuffie, A. R., Austin, B., & French, B. (2021). Teachers’ role in students’ learning at a project-based STEM high school: Implications for teacher education. International Journal of Science and Mathematics Education, 19, 1103–1123. https://doi.org/10.1007/s10763-020-10108-3
Murphy, R. R. (2019). Introduction to AI robotics. MIT press.
Mynbayeva, A., Sadvakassova, Z., & Akshalova, B. (2017). Pedagogy of the Twenty-First Century: Innovative Teaching Methods. In O. Bernad Cavero & N. Llevot-Calvet (Eds.), New Pedagogical Challenges in the 21st Century. InTech. http://dx.doi.org/10.5772/intechopen.72341
Newton, P. M., & Miah, M. (2017). Evidence-based higher education – is the learning styles ‘myth’ important? Frontiers in psychology, 8(444), 2017. https://doi.org/10.3389/fpsyg.2017.00444
Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2. https://doi.org/10.1016/j.caeai.2021.100020
Palmeri, T. J., Love, B. C., & Turner, B. M. (2017). Model-based cognitive neuroscience. Journal of Mathematical Psychology, 76(Part B), 59–64. https://doi.org/10.1016/j.jmp.2016.10.010
Peterson, A. (2018, 5 April). Combinations of pedagogies, innovative and established. OECD Education Working Paper, 172, 33–57.
https://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=EDU/WKP(2018)8&docLanguage=En
Piddubna, O., Maksymchuk, A., Lytvychenko, D., Revutska, O., Moskalenko, M. & Sopina, O. (2023). Implementing neuropedagogical innovation in schools: From theory to practice. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 14(2), 37–58. https://doi.org/10.18662/brain/14.2/443
Piwowarski, M., Uma, S. S., & Nermend, K. (2019). The cognitive neuroscience methods in the analysis of the impact of advertisements in shaping people’s health habits. European Research Studies Journal, XXII(4), 457–471. https://doi.org/10.35808/ersj/1521
Porcelli, S., Van Der Wee, N., van der Werff, S., Aghajani, M., Glennon, J. C., van Heukelum, S., Mogavero, F., Lobo, A., Olivera, F. J., Lobo, E., Posadas, M., Dukart, J., Kozak, R., Arce, E., Ikram, A., Vorstman, J., Bilderbeck, A., Saris, I., Kas, M. J., & Serretti, A. (2019). Social brain, social dysfunction and social withdrawal. Neuroscience & Biobehavioral Reviews, 97, 10–33. https://doi.org/10.1016/j.neubiorev.2018.09.012
Rane, N., Choudhary, S., & Rane, J. (2023). Education 4.0 and 5.0: Integrating Artificial Intelligence (AI) for Personalized and Adaptive Learning. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4638365
Rees, P., Booth, R., & Jones, A. (2016). The emergence of neuroscientific evidence on brain plasticity: Implications for educational practice. Educational and Child Psychology, 33(1), 8–19.
Regan, P. M., & Jesse, J. (2019). Ethical challenges of edtech, big data and personalized learning: Twenty-first century student sorting and tracking. Ethics and Information Technology, 21, 167–179. https://doi.org/10.1007/s10676-018-9492-2
Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (Global Edition). Pearson Education.
Shumiye, Y. D. (2024). Artificial intelligence program implementation in STEM education: Cases of Ethiopia STEM Center. European Journal of Health and Biology Education, 11(1), 7–12.
Šimić, G., Tkalčić, M., Vukić, V., Mulc, D., Španić, E., Šagud, M., Olucha-Bordonau, F. E., Vukšić, M., & Hof, P. R. (2021). Understanding Emotions: Origins and Roles of the Amygdala. Biomolecules, 11, 823. https://doi.org/10.3390/biom11060823
Stahl, B. C., & Wright, D. (2018). Ethics and privacy in Ai and big data: Implementing responsible research and innovation. IEEE Security & Privacy, 16(3), 26–33.
Su, Y.-S., Tseng, W.-L., Cheng, H.-W., & Lai, C.-F. (2024). Applying STEM and extended reality technologies to explore students’ artificial intelligence learning performance and behavior for sustainable development goals. Library Hi Tech. https://doi.org/10.1108/LHT-08-2023-0362
Surianarayanan, C., Lawrence, J. J., Chelliah, P. R., Prakash, E., & Hewage, C. (2023). Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders – A Scoping Review. Sensors, 23(6). https://doi.org/10.3390/s23063062
Taurah, S. P., Bhoyedhur, J., & Sungkur, R. K. (2020). Emotion-Based Adaptive Learning Systems. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12081 LNCS. https://doi.org/10.1007/978-3-030-45778-5_18
Terno, S. (2011). Neuropedagogy: how can it help a history teacher. History and social sciences in schools of Ukraine: theory and methods of teaching, 1, 13–16. http://gorodnia-raygimn-vipsd.edukit.cn.ua/Files/downloads/zurnal_istoriy_11_11_terno.pdf
UNESCO. (2015). Incheon Declaration towards inclusive and equitable quality education and lifelong learning for all. Anyanyelv-pedagógia, 15(3).
United Nations. (2017). The Sustainable Development Goals Report. United Nations Publications. https://doi.org/10.18356/3405d09f-en
Voznyuk, O. V. (2019). Neuropedagogy: a powerful resource for adult education. Andragogical Bulletin, 10, 19–27. http://eprints.zu.edu.ua/31149/1/Вознюк%20andrag_visnyl_10_2019_2020%20последнее_.pdf
Walker, E., Rummel, N., & Koedinger, K. R. (2014). Adaptive intelligent support to improve peer tutoring in algebra. International Journal of Artificial Intelligence in Education, 24(1). https://doi.org/10.1007/s40593-013-0001-9
Wang, N., Tonko, P., Ragav, N., Chungyoun, M., & Plucker, J. (2022). A Perspective on K-12 AI Education. Technology & Innovation, 23(1), 4–14(11). https://doi.org/10.21300/23.1.2023.2
Wilcox, G., Morett, L. M., Hawes, Z., & Dommett, E. J. (2021). Why educational neuroscience needs educational and school psychology to effectively translate neuroscience to educational practice. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.618449
Wright, S. H. (2002, July 10). Papert misses ‘Big Ideas’ of the good old days in AI. MIT News. https://news.mit.edu/2002/papert-misses-big-ideas-good-old-days-ai
Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: A systematic review from 2011 to 2021. International Journal of STEM Education, 9(1). https://doi.org/10.1186/s40594-022-00377-5
Zeer, E. F., & Symanyuk, E. E. (2021). Formation of personalized neuroeducational results of educational activity among students in a vocational school. Izvestia, 27(3), 124–132. https://doi.org/10.15826/izv1.2021.27.3.062
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J. B., Yuan, J., & Li, Y. (2021). A review of Artificial Intelligence (AI) in education from 2010 to 2020. Complexity, 2021. https://doi.org/10.1155/2021/8812542
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