Page 320 - Izobraževanje v dobi generativne umetne inteligence
P. 320

Alenka Žerovnik

                      (2023). ChatGPT for good? On opportunities and challenges of large language
                      models for education. Learning and Individual Differences, 103, 102274.
                  Lawrence, W. (2024). Post-pandemic support for special populations in higher edu-
                      cation through generative artificial intelligence. International Journal of Arts
                      Humanities and Social Science, 5(5), 72–78.
                  Lee, J., Wu, A., Li, D., in Kulasegaram, K. (2021). Artificial intelligence in undergraduate
                      medical education: A scoping review. Academic Medicine, 96(11S), S62–S70.
                  Li, Y., Deng, Y., Peng, B., He, Y., Luo, Y., in Liu, Q. (2024). Generative artificial intelligence
                      in chinese higher education: Chinese undergraduates’ use, perception, and
                      attitudes. Frontiers in Educational Research, 7(4). https://doi.org/10.25236/fer.2024
                      .070401
                  Mahligawati, F. (2023). Artificial intelligence in physics education: A comprehensive
                      literature review. Journal of Physics Conference Series, 2596(1), 012080.
                  Ministrstvo za izobraževanje, znanost in šport Republike Slovenije. (2021). Posodobitev
                      učnih načrtov za osnovne šole. https://www.gov.si/teme/osnovna-sola/
                  Nikolopoulou, K. (2024). Generative artificial intelligence in higher education: Explor-
                      ing ways of harnessing pedagogical practices with the assistance of ChatGPT.
                      International Journal of Changes in Education, 1(2), 103–111.
                  Pack, A. (2024). Using artificial intelligence in tesol: Some ethical and pedagogical
                      considerations. Tesol Quarterly, 58(2), 1007–1018.
                  Penicig, J., Chen, B., Wilson. S., in Garcia, S. (2024). Assessing Explainability in large
                      language models through soft counterfactual analysis: A comparative study of
                      Google Gemini and OpenAI ChatGPT. Research Square. https://doi.org/10.21203
                      /rs.3.rs-5011294/v1
                  Podder, S., in Kumar Singh, S. (2023, 18. oktober). Making generative AI green. Accentu-
                      re. https://www.accenture.com/us-en/blogs/consulting/making-generative-ai
                      -green
                  Pont-Niclos, I. (2024). Creativity and artificial intelligence: A study with prospective
                      teachers. Digital Education Review, 45, 91–97.
                  Preiksaitis, C., in Rose, C. (2023). Generative artificial intelligence in medical education:
                      Opportunities, challenges, and future directions: A scoping review. JMIR Medical
                      Education, 9, e48785–e48785.
                  Revanth Vuruma, S. K., Margetts, A., Su, J., Ahmed, F., in Srivastava, B. (2024, 26. febru-
                      ar). From cloud to edge: Rethinking generative AI for low-resource design challeng-
                      es. ArXiv. https://doi.org/10.48550/arXiv.2402.12702
                  Samek, W., Wiegand, T., in Müller, K.-R. (2017, 28. avgust). Explainable artificial intelli-
                      gence: Understanding, visualizing and interpreting deep learning models. ArXiv.
                      https://doi.org/10.48550/arXiv.1708.08296
                  Sarraf, S. (2024). Evaluating generative AI-enhanced content: A conceptual framework
                      using qualitative, quantitative, and mixed-methods approaches. arXiv. https://
                      arxiv.org/abs/2411.17943



                  320
   315   316   317   318   319   320   321   322   323   324   325