Challenges of Learning in the Age of Artificial Heuristics
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Abstract
This study examines, from a philosophical-educational perspective, the fundamental keys to developing a comprehensive model of human cognition. It considers a wide range of essential
variables, including the multimodality of knowledge, which highlights how different ways of understanding and processing information contribute to a more complete understanding of reality.
The complementary role of languages is also addressed, recognizing that each language offers a
unique perspective in the construction of knowledge. Additionally, the imitative sensorimotor foundation is explored, emphasizing the central role of imitation in learning and cognitive
development, particularly in the early stages of life. Alongside this, cognitive biases are analyzed,
as they influence how we perceive and process information, along with the social influence on
perception, which is crucial for understanding how context and social interactions shape our cognitive experiences. The study focuses particularly on the impacts of AI-based educational chatbots, using ChatGPT as a representative case study. It emphasizes how heuristics, combined with these emerging technologies, contribute to a more adaptive and flexible learning environment, preparing individuals to face the complex challenges of the 21st century in the educational sphere.
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References
Abeliuk, A., & Gutiérrez, C. (2021). Historia y evolución de la Inteligencia Artificial. Revista del Departamento de Ciencias de la Computación de la Universidad de Chile. Bits de Ciencia, 21, 14-21. https://www.dcc.uchile.cl/Bitsdeciencia21.pdf
Alasadi, E. A., & Baiz, C. R. (2023). Generative AI in Education and Research: Opportunities, Concerns, and Solutions. Journal of Chemical Education, 100(8), 2965-2971. https://doi.org/10.1021/acs.jchemed.3c00323
Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023). Transforming Education: A Comprehensive Review of Generative Artificial Intelligence in Educational Settings through Bibliometric and Content Analysis. Sustainability, 15, 12983. https://doi.org/10.3390/su151712983
Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of Artificial Intelligence, 7(1), 52-62. http://dx.doi.org/10.2139/ssrn.4337484
Cooper, G. (2023). Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence. Journal of Science Education and Technology 32, 444–452. https://doi.org/10.1007/s10956-023-10039-y
Damasio, A. (2019). The strange order of things: Life, feeling, and the making of cultures. Vintage.
Dwyer, T.A. (1974). Heuristic strategies for using computers to enrich education. International Journal of Man-Machine Studies 6(2), 137-154. https://doi.org/10.1016/S0020-7373(74)80001-5
Frankfurt, H. G. (2005). On bullshit. Princeton University Press.
Freire, P. (2018). Pedagogia do oprimido. 66ª ed. Rio de Janeiro/São Paulo: Paz e Terra.
Hammerman, R., & Russell, A.L. (2015). Ada’s Legacy: Cultures of Computing from the Victorian to the Digital Age. Association for Computing Machinery and Morgan & Claypool, New York, NY, USA. https://doi.org/10.1145/2809523
Kahneman, D., & Tversky, A. (2013). Prospect theory: An analysis of decision under risk. In Handbook of the fundamentals of financial decision making: Part I (pp. 99-127).
Labadze, L., Grigolia, M., & Machaidze, L. (2023). Role of AI chatbots in education: systematic literature review. International Journal of Educational Technology in Higher Education 20(56). https://doi.org/10.1186/s41239-023-00426-1
Mao, J., Chen, B. & Liu, J.C. (2024). Generative Artificial Intelligence in Education and Its Implications for Assessment. TechTrends 68, 58–66. https://doi.org/10.1007/s11528-023-00911-4
OpenAI©. (2022). Introducing ChatGPT. [Blog], noviembre 30, https://openai.com/blog/chatgpt
Papert, S. A. (2020). Mindstorms: Children, computers, and powerful ideas. Basic books.
Seth, A. (2021). Being you: A new science of consciousness. Penguin.
Thaler, R. H., & Sunstein, C. R. (2009). Nudge: Improving decisions about health, wealth, and happiness. Penguin.
Vallverdú, J. (2019). Blended Cognition: The Robotic Challenge. En: Vallverdú, J., Müller, V. (Eds.) Blended Cognition. Springer Series in Cognitive and Neural Systems, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-03104-6_1
Vermeulen, P. (2022). Autism and the predictive brain: absolute thinking in a relative world. Taylor & Francis.
Zull, J. E. (2020). The art of changing the brain: Enriching the practice of teaching by exploring the biology of learning. Stylus Publishing, LLC.
Zhai, X. (2022). ChatGPT User Experience: Implications for Education. SSRN. http://dx.doi.org/10.2139/ssrn.4312418