Bibliografia

Abercrombie, G., Cercas Curry, A., Dinkar, T., Rieser, V. & Talat, Z. (2023). Mirages: On anthropomorphism in dialogue systems. W Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (s. 4776-4790). Association for Computational Linguistics.

Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö.  & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), e2422633122.

Bender, E. M., Gebru, T., McMillan-Major, A. & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? W Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (s. 610-623). Association for Computing Machinery.

Brady, W. J., Wills, J. A., Jost, J. T., Tucker, J. A. & Van Bavel, J. J. (2017). Emotion shapes the diffusion of moralized content in social networks. Proceedings of the National Academy of Sciences, 114(28), 7313-7318.

Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T. & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354-380.

Chatfield, T. (2025). AI and the future of pedagogy [White paper]. Sage.

Committee on Publication Ethics. (2023). Authorship and AI tools. COPE position statement. https://publicationethics.org/cope-position-statements/ai-author

Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87-114.

De Freitas, J., Uguralp, A. K., Uguralp, Z. O. & Puntoni, S. (2024). AI companions reduce loneliness. Journal of Consumer Research, 52(6), 1126-1148.

Deshpande, A., Rajpurohit, T., Narasimhan, K. & Kalyan, A. (2023). Anthropomorphization of AI: Opportunities and risks. arXiv:2305.14784. https://doi.org/10.48550/arXiv.2305.14784

Deslauriers, L., McCarty, L. S., Miller, K., Callaghan, K. & Kestin, G. (2019). Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom. Proceedings of the National Academy of Sciences, 116(39), 19251-19257.

Etkin, H. K., Etkin, K. J., Carter, R. J. & Rolle, C. E. (2025). Differential effects of GPT-based tools on comprehension of standardized passages. Frontiers in Education, 10, Article 1506752.

Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X. & Gašević, D. (2024). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489-530.

Gimpel, H., Hall, K., Decker, S., Eymann, T., Lämmermann, L., Mädche, A., Röglinger, M., Ruiner, C., Schoch, M., Schoop, M., Urbach, N., & Vandrik, S. (2023). Unlocking the power of generative AI models and systems such as GPT-4 and ChatGPT for higher education: A guide for students and lecturers. Hohenheim Discussion Papers in Business, Economics and Social Sciences, 02-2023. University of Hohenheim.

Giray, L. (2026). When using AI in scientific research: Start with human, end with human. TechTrends, 70(1), 265-272.

Illingworth, S., & Forsyth, R. (2026). GenAI in higher education: Redefining teaching and learning. Bloomsbury Academic.

International Committee of Medical Journal Editors. (2024). Recommendations for the conduct, reporting, editing, and publication of scholarly work in medical journals. https://www.icmje.org/recommendations/

Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A. & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), Article 248.

Lai, L., Pan, Y., Xu, R. & Jiang, Y. (2025). Depression and the use of conversational AI for companionship among college students: The mediating role of loneliness and the moderating effects of gender and mind perception. Frontiers in Public Health, 13, Article 1580826.

Li, P., Yang, J., Islam, M. A. & Ren, S. (2025). Making AI less “thirsty”: Uncovering and addressing the secret water footprint of AI models. Communications of the ACM, 68(7), 54-61

Liang, W., Yuksekgonul, M., Mao, Y., Wu, E. & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), Article 100779.

Lodge, J. M., Howard, S., Bearman, M., Dawson, P., Agostinho, S., Buckingham Shum, S., Deneen, C. & Associates. (2023). Assessment reform for the age of artificial intelligence. Tertiary Education Quality and Standards Agency. https://www.teqsa.gov.au/sites/default/files/2023-09/assessment-reform-age-artificial-intelligence-discussion-paper.pdf

Long, D. & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. W Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (s. 1-16). Association for Computing Machinery.

Luccioni, A. S., Jernite, Y. & Strubell, E. (2024). Power hungry processing: Watts driving the cost of AI deployment? W Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (s. 85-99). Association for Computing Machinery.

Miao, F. & Cukurova, M. (2024). AI competency framework for teachers. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000391104

Miao, F. & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693

Miao, F. & Shiohira, K. (2024). AI competency framework for students. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000391105

Mollick, E. R. & Mollick, L. (2023). Assigning AI: Seven Approaches for Students, with Prompts (September 23, 2023). The Wharton School Research Paper, Available at SSRN: https://ssrn.com/abstract=4475995 or http://dx.doi.org/10.2139/ssrn.4475995

Ng, D. T. K., Leung, J. K. L., Chu, S. K. W. & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, Article 100041.

Oakley, B., Johnston, M., Chen, K.-Z., Jung, E., & Sejnowski, T. J. (2025). The memory paradox: Why our brains need knowledge in an age of AI. In M. Rangeley & N. Fairfax (Eds.), The artificial intelligence revolution: Challenges and opportunities (pp. 573–628). Springer.

Perkins, M., Furze, L., Roe, J. & MacVaugh, J. (2024). The Artificial Intelligence Assessment Scale (AIAS): A framework for ethical integration of generative AI in educational assessment. Journal of University Teaching and Learning Practice, 21(6), 49-66.

Perkins, M., Jasper, R., & Furze, L. (2025). Reimagining the Artificial Intelligence Assessment Scale: A refined framework for educational assessment. Journal of University Teaching and Learning Practice, 22(7).

Perkins, M., & Roe, J. (2025). The end of assessment as we know it: GenAI, inequality and the future of knowing. In UNESCO (Ed.), AI and the future of education: Disruptions, dilemmas and directions (pp. 76–80). UNESCO.

Peters, U. & Chin-Yee, B. (2025). Generalization bias in large language model summarization of scientific research. Royal Society Open Science, 12(4), Article 241776.

Politechnika Gdańska. (2024). Wytyczne dotyczące stosowania narzędzi generatywnej sztucznej inteligencji na Politechnice Gdańskiej. Załącznik do Pisma Okólnego Rektora Politechniki Gdańskiej nr 29/2024.

Richardson, I. (2025, November 11). Universities risk irrelevance by failing to engage fully with AI. Times Higher Education. 

Risko, E. F. & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676-688.

Scholich, T., Barr, M., Wiltsey Stirman, S., & Raj, S. (2025). A comparison of responses from human therapists and large language model-based chatbots to mental health scenarios. JMIR Mental Health, 12, Article e69709.

Sharma, M., Tong, M., Korbak, T., Duvenaud, D., Askell, A., Bowman, S. R., Cheng, N., Durmus, E., Hatfield-Dodds, Z., Johnston, S. R., Kravec, S., Maxwell, T., McCandlish, S., Ndousse, K., Rausch, O., Schiefer, N., Yan, D., Zhang, M., & Perez, E. (2023). Towards understanding sycophancy in language models. In The Twelfth International Conference on Learning Representations. https://arxiv.org/abs/2310.13548  

Soderstrom, N. C. & Bjork, R. A. (2015). Learning versus performance: An integrative review. Perspectives on Psychological Science, 10(2), 176-199.

Stromberg, D., Lei, V., & Wu, Y. (2026). The Generative AI Learning Penalty: Evidence from Chinese Secondary Education [preprint] SSRN: https://ssrn.com/abstract=6868618

Tang, L., Sun, Z., Idnay, B., Nestor, J. G., Soroush, A., Elias, P. A. & in. (2023). Evaluating large language models on medical evidence summarization. npj Digital Medicine, 6, Article 158.

UNESCO. (2021). Recommendation on the ethics of artificial intelligence. https://unesdoc.unesco.org/ark:/48223/pf0000381137

UNESCO Women for Ethical AI. (2024). Outlook study on artificial intelligence and gender. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000391719

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł. & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30(pp. 5998-6008). Curran Associates.

Vosoughi, S., Roy, D. & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151.

Wang, P., Zhang, L.-Y., Tzachor, A. & Chen, W.-Q. (2024). E-waste challenges of generative artificial intelligence. Nature Computational Science, 4(11), 818-823.

Wardle, C. & Derakhshan, H. (2017). Information disorder: Toward an interdisciplinary framework for research and policy making. Council of Europe. https://shorensteincenter.org/wp-content/uploads/2017/10/Information-Disorder-Toward-an-interdisciplinary-framework.pdf

Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., Šigut, P. & Waddington, L. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19, Article 26.

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64-70.