Formazione & insegnamento
ISSN: 2279-7505 | Published: 2024-09-21
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Title: Integrating ChatGPT as a Learning Tool: Potential Benefits and Critical Considerations
Abstract: This article examines the potential integration of OpenAI's ChatGPT into educational settings, with a particular focus on its capacity to enhance students' engagement in learning contexts. In order to investigate the potential of ChatGPT to deliver tailored educational content to diverse learning groups, structured interviews were conducted between different types of students and the chatbot. These interviews were designed to simulate hypothetical basic real-world educational interactions. While the outcomes indicate that ChatGPT is capable of adapting to a range of educational needs and styles, thereby facilitating more accessible and engaging learning experiences, however, a lot of limitations such as the lack of emotional intelligence and the potential to reduce critical thinking have been identified—underscoring the necessity for cautious integration and ongoing monitoring of AI technologies in education settings. The researcher proposes a balanced approach to AI integration, emphasizing the potential for synergy between AI tools and traditional learning methods.
Keywords: Artificial Intelligence; ChatGPT; Digital education; Learning Environment; Media education
Title: Integrare ChatGPT come strumento di apprendimento: Potenziali benefici e considerazioni critiche
Abstract: Questo articolo esamina la potenziale integrazione di ChatGPT di OpenAI nei contesti educativi, con particolare attenzione alla sua capacità di migliorare l'impegno degli studenti nei contesti di apprendimento. Per indagare il potenziale di ChatGPT nel fornire contenuti formativi personalizzati a gruppi di apprendimento diversificati, sono state condotte interviste strutturate tra diversi tipi di studenti e il chatbot. Queste interviste sono state progettate per simulare interazioni educative ipotetiche di tipo elementare che rispecchino situazioni reali. Sebbene i risultati indichino che ChatGPT è capace di adattarsi a una gamma di esigenze e stili educativi, facilitando lo sviluppo di esperienze di apprendimento più accessibili e coinvolgenti, sono state identificate numerose limitazioni, quali la mancanza di intelligenza emotiva e il potenziale di diminuire il pensiero critico – sottolineando la necessità di un'integrazione cauta e di un monitoraggio continuo delle tecnologie IA nei contesti educativi. Il ricercatore propone un approccio equilibrato all'integrazione dell'IA, enfatizzando il potenziale di sinergia tra gli strumenti IA e i metodi di apprendimento tradizionali.
Keywords: Apprendimento coinvolgente; ChatGPT; Educazione ai media; Intelligenza Artificiale; Tecnologie digitali
Title: Intégration de Chatgpt comme outil d'apprentissage: avantages potentiels et considérations critiques
Abstract: Cet article examine l'intégration potentielle du Chatgpt d'Openai dans les contextes éducatifs, avec un accent particulier sur sa capacité à améliorer l'engagement des étudiants dans des contextes d'apprentissage.Afin d'étudier le potentiel de Chatgpt pour livrer un contenu éducatif sur mesure à divers groupes d'apprentissage, des entretiens structurés ont été menés entre différents types d'étudiants et le chatbot.Ces entretiens ont été conçus pour simuler des interactions éducatives de base hypothétiques de base.Bien que les résultats indiquent que Chatgpt est capable de s'adapter à une gamme de besoins et de styles éducatifs, facilitant ainsi des expériences d'apprentissage plus accessibles et engageantes, cependant, de nombreuses limitations telles que le manque d'intelligence émotionnelle et le potentiel de réduire la pensée critique ont été identifiées - obligeant le nécessité d'une intégration prudente et d'une surveillance en cours des technologies de l'AI dans les paramètres de l'éducation.Le chercheur propose une approche équilibrée de l'intégration de l'IA, soulignant le potentiel de synergie entre les outils d'IA et les méthodes d'apprentissage traditionnelles. (This version of record did not originally feature translated metadata in this target language; the translation is hereby provided by Google Translation)
Keywords: Intelligence artificielle;Chatgpt;Éducation numérique;Environnement d'apprentissage;Éducation des médias
Title: Integración de ChatGPT como herramienta de aprendizaje: beneficios potenciales y consideraciones críticas
Abstract: Este artículo examina la integración potencial de ChatGPT de OpenAI en entornos educativos, con un enfoque particular en su capacidad para mejorar el compromiso de los estudiantes en contextos de aprendizaje. Para investigar el potencial de ChatGPT para ofrecer contenido educativo personalizado a grupos de aprendizaje diversos, se realizaron entrevistas estructuradas entre diferentes tipos de estudiantes y el chatbot. Estas entrevistas fueron diseñadas para simular interacciones educativas reales básicas hipotéticas. Aunque los resultados indican que ChatGPT es capaz de adaptarse a una variedad de necesidades y estilos educativos, facilitando experiencias de aprendizaje más accesibles y atractivas, se han identificado muchas limitaciones, como la falta de inteligencia emocional y el potencial de reducir el pensamiento crítico, subrayando la necesidad de una integración cautelosa y un seguimiento continuo de las tecnologías de IA en los entornos educativos. El investigador propone un enfoque equilibrado para la integración de la IA, enfatizando el potencial de sinergia entre las herramientas de IA y los métodos de aprendizaje tradicionales.
Keywords: Aprendizaje atractivo; ChatGPT; Educación mediática; Inteligencia Artificial; Tecnologías digitales
Title: Integrando ChatGPT como ferramenta de aprendizado: benefícios potenciais e considerações críticas
Abstract: Este artigo examina a integração potencial do ChatGPT da OpenAI em ambientes educacionais, com foco particular em sua capacidade de aumentar o engajamento dos alunos em contextos de aprendizado. Para investigar o potencial do ChatGPT em entregar conteúdo educacional personalizado para grupos de aprendizagem diversos, entrevistas estruturadas foram conduzidas entre diferentes tipos de alunos e o chatbot. Estas entrevistas foram desenhadas para simular interações educacionais reais básicas hipotéticas. Embora os resultados indiquem que o ChatGPT é capaz de se adaptar a uma variedade de necessidades e estilos educacionais, facilitando experiências de aprendizado mais acessíveis e envolventes, muitas limitações, como a falta de inteligência emocional e o potencial de reduzir o pensamento crítico, foram identificadas — destacando a necessidade de uma integração cuidadosa e monitoramento contínuo das tecnologias de IA em ambientes educacionais. O pesquisador propõe uma abordagem equilibrada para a integração da IA, enfatizando o potencial de sinergia entre ferramentas de IA e métodos de aprendizado tradicionais.
Keywords: Aprendizado envolvente; ChatGPT; Educação midiática; Inteligência Artificial; Tecnologias digitais
Bender, E. M., & Koller, A. (2020). Climbing towards NLU: On meaning, form, and understanding in the age of data. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 5185–5198). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.463
Bommasani, R., Hudson, D. A., Adeli, E., et al. (2021). On the opportunities and risks of foundation models. arXiv. https://doi.org/10.48550/arXiv.2108.07258
Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience, and school (Expanded ed.). National Academy Press.
Brown, T. B., Mann, B., Ryder, N., et al. (2020). Language models are few-shot learners. arXiv. https://doi.org/10.48550/arXiv.2005.14165
Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 77–91). https://doi.org/10.1145/3287560.3287596
Chan, C. K. Y., & Tsi, L. H. (2023). The AI revolution in education: Will AI replace or assist teachers in higher education? arXiv. https://doi.org/10.48550/arXiv.2305.01185
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE Publications.
Denzin, N. K., & Lincoln, Y. S. (Eds.). (2018). The SAGE handbook of qualitative research (5th ed.). SAGE Publications.
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv. https://doi.org/10.48550/arXiv.1810.04805
Edwards, L., & Veale, M. (2017). Slave to the algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for. Duke Law & Technology Review, 16(1), 18–84. https://doi.org/10.2139/ssrn.2972855
Floridi, L. (2023). The ethics of artificial intelligence. Oxford University Press.
Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681–694. https://doi.org/10.1007/s11023-020-09548-1
Fuchs, T. (2018). Ecology of the brain: The phenomenology and biology of the embodied mind. Oxford University Press.
Gligorea, I., Marius, C., Romana, O., Andra-Teodora, G., Hortensia, G., & Paul, R. (2023). Adaptive learning using artificial intelligence in e-learning: A literature review. Education Sciences, 13(12), 1216. https://doi.org/10.3390/educsci13121216
Goleman, D. (2005). Emotional intelligence: Why it can matter more than IQ. Random House.
Grassini, S. (2023). Shaping the future of education: Exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), 692. https://doi.org/10.3390/educsci13070692
Haider, J., & Sundin, O. (2019). Invisible search and online search engines: The ubiquity of search in everyday life (1st ed.). Routledge. https://doi.org/10.4324/9780429448546
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Husserl, E. (1970). The crisis of European sciences and transcendental phenomenology: An introduction to phenomenological philosophy. Northwestern University Press.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lee, J. (2020). Mental health effects of school closures during COVID-19. The Lancet Child & Adolescent Health, 4(6), 421. https://doi.org/10.1016/S2352-4642(20)30109-7
Luckin, R. (2017). Machine learning and human intelligence: The future of education for the 21st century. UCL IOE Press.
Martin, A. (2018). Digital literacy and the 'digital society.' In Digital literacies: Concepts, policies and practices (pp. 151-176). Routledge.
Merleau-Ponty, M. (1962). Phenomenology of perception. Routledge.
Montenegro-Rueda, M., Fernández-Cerero, J., Fernández-Batanero, J. M., & López-Meneses, E. (2023). Impact of the implementation of ChatGPT in education: A systematic review. Computers, 12(8), 153. https://doi.org/10.3390/computers12080153
Nissenbaum, H. (2011). Privacy in context: Technology, policy, and the integrity of social life. Stanford University Press.
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
Peters, M. E., Neumann, M., Iyyer, M., et al. (2018). Deep contextualized word representations. arXiv. https://doi.org/10.48550/arXiv.1802.05365
Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3(3), 417–424. https://doi.org/10.1017/S0140525X00005756
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. British Journal of Educational Technology, 50(6), 1373–1384. https://doi.org/10.1111/bjet.12831
Suchman, L. A. (2007). Human-machine reconfigurations: Plans and situated actions. Cambridge University Press.
Turkle, S. (2011). Alone together: Why we expect more from technology and less from each other. Basic Books.
Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. https://doi.org/10.48550/arXiv.1706.03762
Voigt, P., & Von dem Bussche, A. (2017). The EU General Data Protection Regulation (GDPR): A practical guide (1st ed.). Springer. https://doi.org/10.1007/978-3-319-57959-7
Wolf, T., Debut, L., Sanh, V., et al. (2020). Transformers: State-of-the-art natural language processing. arXiv. https://doi.org/10.48550/arXiv.1910.03771
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0
Ziegler, D. M., Stiennon, N., Wu, J., et al. (2019). Fine-tuning language models from human preferences. arXiv. https://doi.org/10.48550/arXiv.1909.08593
van Manen, M. (2023). Phenomenology of practice: Meaning-giving methods in phenomenological research and writing (2nd ed.). Routledge. https://doi.org/10.4324/9781003228073