Alumnado subrepresentado e inteligencia artificial
DOI:
https://doi.org/10.31637/epsir-2025-843Palabras clave:
Inteligencia Artificial, machine learning, educación, subrepresentación, discriminación, inclusión, derechos fundamentales, Políticas públicasResumen
Introducción: Los educadores, la administración pública y los gobiernos, deben ser conscientes de las fortalezas y debilidades de la IA en el aprendizaje, a fin de ser empoderados, no dominados por la tecnología en las prácticas de educación para la ciudadanía digital, especialmente con minorías y/o estudiantes subrepresentados, porque podría aumentar la brecha social y digital. Metodología: Este estudio, utiliza la metodología PRISMA y analiza datos obtenidos de la Web of Science y Google Scholar. Resultados: Se analiza si se producen errores, sesgos, subrepresentación y discriminación, o estos sistemas contribuyen a la inclusión; su interés en la comunidad científica y principales desafíos normativos y éticos a través de numerosos ejemplos. Discusión: Los hallazgos subrayan la importancia de su implementación, de la escasez de la investigación en este ámbito, las oportunidades, las prácticas nocivas y sus efectos, y los retos por alcanzar. Conclusiones: Este análisis subraya su efecto en otros ámbitos como el laboral, su importancia en relación a los derechos fundamentales, y la afectación a nuestros propios modelos de Estado social y democrático de derecho.
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Access Now (2018). Human rights in the age of artificial intelligence. AccessNow. bit.ly/4bITs0r
Anuradha, J., Tisha, Ramachandran, V., Arulalan, K. V. y Tripathy, B. K. (2010). Diagnosis of ADHD using SVM algorithm. Proceedings of the Third Annual ACM Bangalore Conference. 1-4. https://doi.org/10.1145/1754288.1754317 DOI: https://doi.org/10.1145/1754288.1754317
APC (Association for Progressive Communications) (2019). Artificial intelligence: human rights, social justice and development. Global Informat. Society Watch. https://goo.su/697XaJa
Arnold K. E. y Pistilli M. D. (2012). Course signals at Purdue: using learning analytics to increase student success. LAK12: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 267-70. https://doi.org/10.1145/2330601.2330666 DOI: https://doi.org/10.1145/2330601.2330666
Ashrafi F. y Javadi A. (2024). Correct characteristics of the newly involved artificial intelligence methods in science and technology using statistical data sets, International Journal of Modern Engineering Technologies 1(1), 1,13. https://icdst.ir/OAJ/index.php/IJMET/article/view/30
Asimov, I. (1942). Runaround, Astounding Science Fiction. Street y Smith
Baker, R. S. y Hawn, A. (2021). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 1-41. https://doi.org/10.35542/osf.io/pbmvz DOI: https://doi.org/10.35542/osf.io/pbmvz
BBC (2020). A-levels: Algorithm at centre of grading crisis 'unlawful' says Labour. BBC News https://www.bbc.com/news/uk-politics-53837722
Benaich, N. (2020). AI has disappointed on Covid, Financ. Times. https://acortar.link/KcpeTZ
Bietti, E. (2020). From ethics washing to ethics bashing: a view on tech ethics from within moral philosophy. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 210-19. https://dl.acm.org/doi/abs/10.1145/3351095.3372860 DOI: https://doi.org/10.1145/3351095.3372860
Biggs P. et al. (2018). The state of broadband 2018: broadband catalyzing sustainable. http://handle.itu.int/11.1002/pub/810d0472-en
Borji, A. (2023). A categorical archive of chatgpt failures. https://doi.org/10.48550/arXiv.2302.03494 DOI: https://doi.org/10.21203/rs.3.rs-2895792/v1
Brown, L. X. (2020). How automated test proctoring software discriminates against disabled students. Center for Democracy and Technology. bit.ly/4f7dm8z
Byrne, R., Tang, M., Truduc, J. y Tang, M. (2010). eGrader, a software application that automatically scores student essays: with a postscript on the ethical complexities. Journal of Systemics, Cybernetics & Informatics, 8(6), 30-35. https://www.iiisci.org/journal/pdv/sci/pdfs/MJ910TT.pdf
Boccanfuso, L., Barney, E., Foster, C., Ahn, Y. A., Chawarska, K., Scassellati, B. y Shic, F. (2016). Emotional robot to examine different play patterns and affective responses of children with and without ASD. 11th ACM/IEEE Intern. Conf. on Human-Robot Inter. (HRI), 19-26. IEEE. https://doi.org/10.1109/HRI.2016.7451729 DOI: https://doi.org/10.1109/HRI.2016.7451729
Bogen, M. y Rieke, A. (2018). Help Wanted: An Examination of Hiring Algorithms, Equity, and Bias, Upturn, 26–39. https://www.upturn.org/reports/2018/hiring-algorithms/
Cano, A. y Leonard, J. D. (2019). Interpretable multiview early warning system adapted to underrepresented student populations. IEEE Transactions on Learning Technologies, 12(2), https://doi.org/198-211.10.1109/TLT.2019.2911079 DOI: https://doi.org/10.1109/TLT.2019.2911079
Centre for Data Ethics and Innovation (2020). Review into bias in algorithmic decision-making. bit.ly/4d1JuZb
Chrysafiadi K. y Virvou M. (2013). Student modeling approaches: a literature review for the last decade. Expert Systems with Applications 40(11), 4715-4729. https://doi.org/10.1016/j.eswa.2013.02.007 DOI: https://doi.org/10.1016/j.eswa.2013.02.007
Conijn, R., Kleingeld, A., Matzat, U. y Snijders, C. (2022). The fear of big brother: The potential negative side‐effects of proctored exams. Journ. of Comp. Assisted Learn., 38(6), 1521-1534. https://doi.org/10.1111/jcal.12651 DOI: https://doi.org/10.1111/jcal.12651
Cotterell, R., Mielke, S. J., Eisner, J. y Roark, B. (2018). Are all languages equally hard to language-model? http://arxiv.org/abs/1806.03743
Crawford K. (2021). Atlas of AI: power, politics, and the planetary costs of artificial intelligence. Yale University Press. DOI: https://doi.org/10.12987/9780300252392
Cyndecka M. A. (2020). A dystopian story about Covid-19, artificial intelligence setting grades and the GDPR. EFTA-Studies. bit.ly/467EMqB
Dalipi F., Imran A. S. y Kastrati Z. (2018). MOOC dropout prediction using machine learning techniques. EDUCON, 1007-1014. https://doi.org/10.1109/EDUCON.2018.8363340 DOI: https://doi.org/10.1109/EDUCON.2018.8363340
Dennis M. J. (2018). Artificial intelligence and recruitment, admission, progression, and retention. Enrollment Management Report, 22(9), 1-3, https://doi.org/10.1002/emt.30479 DOI: https://doi.org/10.1002/emt.30479
Dixon, L., Li, J., Sorensen, J., Thain, N. y Vasserman, L. (2018). Measuring and mitigating unintended bias in text classification. AAAI/ACM Confer. on AI, Ethics, and Society, 67-73. https://doi.org/10.1145/3278721.3278729 DOI: https://doi.org/10.1145/3278721.3278729
Drigas, A. y Ioannidou, R. E. (2012). Artificial Intelligence in Special Education: A Decade Review. International Journal of Engineering Education 28(6), 1366-1372. bit.ly/3LqyQ2m
Duncan, D., Garner, R., Bennett, A., Sinclair, M., Ramirez-de la Cruz, G. y Pasik-Duncan, B. (2022). Interdisciplinary K-12 Control Education in Biomedical and Public Health Applications. IFAC-PapersOnLine, 55(17), 242-248. https://doi.org/10.1016/j.ifacol.2022.09.286 DOI: https://doi.org/10.1016/j.ifacol.2022.09.286
Feng W., Tang J. y Liu T. X. (2019). Understanding dropouts in MOOCs. Proc. of the AAAI Conf. on AI, 33(1), 517-24. https://doi.org/10.1609/aaai.v33i01.3301517 DOI: https://doi.org/10.1609/aaai.v33i01.3301517
Finkelstein, S., Yarzebinski, E., Vaughn, C., Ogan, A., y Cassell, J. (2013). The effects of culturally congruent educational technologies on student achievement. Intern. Conf. on Artificial Intelligence in Education. (pp.493-502). Springer. DOI: https://doi.org/10.1007/978-3-642-39112-5_50
García-Bullé, S. (2021). The dark side of online exam proctoring. Observatorio de Innov. Educativa. https://observatory.tec.mx/edu-news/dark-side-proctored-exams
Gin, B. C., Ten Cate, O., O’Sullivan, P. S. y Boscardin, C. (2024). Assessing supervisor versus trainee viewpoints of entrustment through cognitive and affective lenses. Advances in Health Sciences Education, 1-22. https://doi.org/10.1007/s10459-024-10311-9 DOI: https://doi.org/10.1007/s10459-024-10311-9
Goel Y. y Goyal R. (2020). On the effectiveness of self-training in MOOC dropout Prediction. Open Computer Sci, 10(1), 246-58. https://doi.org/10.1515/comp-2020-0153 DOI: https://doi.org/10.1515/comp-2020-0153
Ghosh, S., Baker, D., Jurgens, D. y Prabhakaran, V. (2021). Detecting cross-geographic biases in toxicity modeling on social media. https://arxiv.org/pdf/2104.06999 DOI: https://doi.org/10.18653/v1/2021.wnut-1.35
Gray J. (2017). University of Buckingham to monitor students’ social media accounts to tackle depression and suicide. HuffPost UK. bit.ly/468zAD6
Gu, J. y Ming, X. (2023). Social discrimination and college enrollment. Asia Pacific Educ. Review, 24(1), 57-69. https://doi.org/10.1007/s12564-021-09725-6 DOI: https://doi.org/10.1007/s12564-021-09725-6
Guilbaud, P. y Hirsch, M. J. (2022, June). Promoting Equity and Achievement in Real-Time Learning (PEARL). International Conference on Human-Computer Interaction (pp. 159-173). Springer. https://doi.org/10.1007/978-3-031-05887-5_12 DOI: https://doi.org/10.1007/978-3-031-05887-5_12
Hartmann, J., Schwenzow, J. y Witte, M. (2023). The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation. https://doi.org/10.48550/arXiv.2301.01768 DOI: https://doi.org/10.2139/ssrn.4316084
Heaven, D. (2019). Why deep-learning AIs are so easy to fool. Nature, 574 (7777), 163-166. https://doi.org/10.1038/d41586-019-03013-5 DOI: https://doi.org/10.1038/d41586-019-03013-5
Heaven W. D. (2021). Hundreds of AI tools have been built to catch covid. None of them helped, MIT Technology Review. bit.ly/4cEaIoV
Hendry J. (2018, Junio, 30). Govts dump NAPLAN robo marking plans. ITnews. www.itnews.com.au/news/govts-dump-naplan-robo-marking-plans-482044
Hickey, S. y Hossain, N. (Eds) (2019). The Politics of Education in Developing Countries: From Schooling to Learning. Oxford. https://doi.org/10.1093/oso/9780198835684.001.0001 DOI: https://doi.org/10.1093/oso/9780198835684.001.0001
Holmes W. (2020, Febrero, 21). The right kind of AI in education. Nesta. bit.ly/3xVK3Fa
Holmes, W., Persson, J., Chounta, I.A., Wasson, B., y Dimitrova, V. (2019) Artificial intelligence and education - A critical view through the lens of human rights, democracy and the rule of law. Council of Europe Publishing. bit.ly/3W3v6Jg
Holstein, K. y Doroudi, S. (2021). Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education? https://doi.org/10.48550/arXiv.2104.12920 DOI: https://doi.org/10.4324/9780429329067-9
Hutchinson, B., Prabhakaran, V., Denton, E., Webster, K., Zhong, Y. y Denuyl, S. (2020). Social biases in NLP models as barriers for pers. with disabilities. https://arxiv.org/pdf/2005.00813 DOI: https://doi.org/10.18653/v1/2020.acl-main.487
Hutson, M. (2021). Robo-writers: the rise and risks of language-generating AI. Nature, 591(7848). 22-25. https://doi.org/10.1038/d41586-021-00530-0 DOI: https://doi.org/10.1038/d41586-021-00530-0
Hwang, G. J., Xie, H., Wah, B. W. y Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: AI, 1(100001). https://doi.org/10.1016/j.caeai.2020.100001 DOI: https://doi.org/10.1016/j.caeai.2020.100001
Ilkka, T. (2018). The impact of artificial intelligence on learning, teaching, and education. European Union. https://hdl.handle.net/20.500.12799/6021
Jakobsson, J. (2017, Marzo, 29). Nya miljoner ska ta deras dyslexi-startup till USA. Di Digital. bit.ly/3xXHRwM
Johnson, A. (2023, Febrero, 03). Is ChatGPT Partisan? Poems About Trump And Biden Raise Questions About The AI Bot’s Bias—Here’s What Experts Think. Forbes. bit.ly/3xXFFFA
Kocoń, J., Cichecki, I., Kaszyca, O., Kochanek, M., Szydło, D., Baran, J., Bielaniewicz, J., Gruza, M., Janz, A., Kanclerz, K., Kocoń, A., Koptyra, B., Mieleszczenko-Kowszewicz, W., Miłkowski, P., Oleksy, M.,
Piasecki, M., Radliński, L., Wojtasik, K., Woźniak, S. y Kazienko, P. (2023). ChatGPT: Jack of all trades, master of none. Information Fusion, 99, 101861. https://doi.org/10.1016/j.inffus.2023.101861 DOI: https://doi.org/10.1016/j.inffus.2023.101861
Kohli, M. y Prasad, T. V. (2010, June). Identifying dyslexic students by using artificial neural networks. Proceedings of the world congress on engineering, 1(1). 1-4. https://www.iaeng.org/publication/WCE2010/WCE2010_pp118-121.pdf
Kröplin, J., Maier, L., Lenz, J. H. y Romeike, B. (2024). Knowledge Transfer and Networking Upon Implementation of a Transdisciplinary Digital Health Curriculum in a Unique Digital Health Training Culture. JMIR Medical Education, 10, e51389. https://doi.org/10.2196/51389 DOI: https://doi.org/10.2196/51389
Kurzweil, R. (2005). The singularity is near. In Ethics and emerging technologies, 393-406. Palgrave Macmillan UK. https://doi.org/10.1057/9781137349088_26 DOI: https://doi.org/10.1057/9781137349088_26
Leaton S. H. y Kucirkova N. (2018). A united and thriving Europe? A sociology of the European Schools’ and ‘If personalised education and artificial intelligence are a democratic problem, could pluralisation be the democratic solution? Proceedings of British Educational Research Association (BERA) Annual Conference, 11-13. https://acortar.link/1b5e4T
Ledford, H. (2019). Millions affected by racial bias in health-care algorithm. Nature, 574(31), 608-609. https://doi.org/10.1038/d41586-019-03228-6 DOI: https://doi.org/10.1038/d41586-019-03228-6
Lundy, L., Byrne, B., Templeton, M. y Lansdown, G. (2019). Two clicks forward and one click back. Council of Europe. bit.ly/4d1RCsy
Marcinkowski, F., Kieslich, K., Starke, C. y Lünich, M. (2020). Implications of AI (un-)fairness in higher education admissions. Conference on Fairness, Accountability, and Transparency, 122-130. https://doi.org/10.1145/3351095.3372867 DOI: https://doi.org/10.1145/3351095.3372867
Marcus, G. y Davis, E. (2020). GPT-3, Bloviator: OpenAI’s language generator has no idea what it’s talking about. MIT Technology Review. bit.ly/3y0x84J
Montero, S. (2021, Marzo, 27). Los algoritmos y sus sesgos de género, raza o clase: así te perjudican en la búsqueda de trabajo o de ayudas sociales. Público. bit.ly/4d0PYaR
Morozov E. (2014). To save everything, click here: technology, solutionism, and the urge to fix problems that don’t exist. Penguin.
Naismith, B. y Juffs, A. (2021). Finding the sweet spot: Learners’ productive knowledge of mid-frequency lexical items. Language Teaching Research, 28(3), 1106-1142. https://doi.org/10.1177/13621688211020412 DOI: https://doi.org/10.1177/13621688211020412
Newton, D. (2021). Artificial Intelligence grading your ‘neuroticism’? Welcome to colleges' new frontier. USA Today. bit.ly/4bPl18u
Nixon, N., Lin, Y. y Snow, L. (2024). Catalyzing Equity in STEM Teams: Harnessing Generative AI for Inclusion and Diversity. Policy Insights from the Behavioral and Brain Sciences, 11(1), 85-92. https://doi.org/10.1177/23727322231220356 DOI: https://doi.org/10.1177/23727322231220356
Nkambou, R., Azevedo, R. y Vassileva, J. (Eds.). (2018). Intelligent Tutoring Systems: 14th International Conference. ITS 2018, Montreal, QC, Canada, Proc. Springer. DOI: https://doi.org/10.1007/978-3-319-91464-0
Nazaretsky, T., Cukurova, M. y Alexandron, G. (2022, March). An instrument for measuring teachers’ trust in AI-based educational technology. LAK22: 12th intern. learning analytics and knowledge conference. https://doi.org/10.1145/3506860.3506866 DOI: https://doi.org/10.1145/3506860.3506866
Ojha, V., Perdriau, C., Lagesse, B. y Lewis, C. M. (2023). Computing Specializations. 54th ACM Technical Symposium on Computer Science Education, 1, 966-972. https://doi.org/10.1145/3545945.3569782 DOI: https://doi.org/10.1145/3545945.3569782
Okolo, C. T. (2024). Beyond AI Hype: A Hands-on Workshop Series for Enhancing AI Literacy in Middle and High School Students. RESPECT 2024: Proceedings of the 2024 on RESPECT Annual Conference (pp. 86-93). https://doi.org/10.1145/3653666.3656075 DOI: https://doi.org/10.1145/3653666.3656075
Olney, A. M., Donnelly, P. J., Samei, B. y D'Mello, S.K. (2017). Assessing the Dialogic Properties of Classroom Discourse. Data Min Soc. 162-67. https://files.eric.ed.gov/fulltext/ED596578.pdf
Oskotsky, T., Bajaj, R., Burchard, J., Cavazos, T., Chen, I., Connell, W. T., Eaneff, S. y Sirota, M. (2022). Nurturing diversity and inclusion in AI in Biomedic. through a virtual summer program for high school students. PLoS comput. biology, 18(1), e1009719. https://doi.org/10.1371/journal.pcbi.1009719 DOI: https://doi.org/10.1371/journal.pcbi.1009719
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., […] y Mulrow, C.D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372(71), 1-9. https://doi.org/10.1136/bmj.n71 DOI: https://doi.org/10.1136/bmj.n71
Pinkwart, N. (2016). Another 25 years of AIED? Challenges and opportunities for intelligent educational technologies of the future. International journal of artificial intelligence in education, 26, 771-783. https://doi.org/10.1007/s40593-016-0099-7 DOI: https://doi.org/10.1007/s40593-016-0099-7
Popenici S.A.D. y Kerr S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology, Enhanced Learning 12 (22). https://doi.org/10.1186/s41039-017-0062-8 DOI: https://doi.org/10.1186/s41039-017-0062-8
Porayska-Pomsta, K., Alcorn, A. M., Avramides, K., Beale, S., Bernardini, S., Foster, M. E., […] y Smith, T. J. (2018). Blending human and AI to support autistic children’s social commun. skills. TOCHI, 25(6), 1-35. https://doi.org/10.1145/3271484 DOI: https://doi.org/10.1145/3271484
Powles J. (2018). The seductive diversion of ‘solving’ bias in artificial intelligence, OneZero. bit.ly/3Sb6i0O
PRISMA. (s. f.). PRISMA 2020 Checklist. https://prisma.shinyapps.io/checklist/
Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., [...] y Ng, A. Y. (2017). Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. http://arxiv.org/abs/1711.05225
Retzlaff, N. (2024). Political Biases of ChatGPT in Different Languages. Preprints 2024, 2024061224 https://doi.org/10.20944/preprints202406.1224.v1 DOI: https://doi.org/10.20944/preprints202406.1224.v1
Ricoy-Casas, R. M. (2019). Inteligencia artificial y políticas públicas en la UE. Derecho, desarrollo y nuevas tecnologías (pp. 187-234). Thomson Reuters Aranzadi.
Ricoy-Casas, R. M. (2021a). Sesgos y algoritmos: inteligencia de género y Algunos dilemas éticos en la utilización de la inteligencia artificial y los algoritmos. En P.R. Bonorino-Ramírez, R. Fernández, y P. Valcárcel (Dirs.). Nuevas normatividades: inteligencia artificial, derecho y género. Thomson Reuters Aranzadi.
Ricoy-Casas, R. M. (2021b). Inteligencia artificial y administración de justicia: una política pública sub iudice. .R. Bonorino-Ramírez, R. Fernández, y P. Valcárcel e I.S. García (Dirs.). Justicia, administración y derecho. Thomson Reuters Aranzadi.
Ricoy-Casas, R. M. (2022). The Metaverse as a New Space for Political Communication. En P.C. López-López, D. Barredo, Á. Torres-Toukoumidis, A. De-Santis y Ó. Avilés (eds) Communication and Applied Technologies. Smart Innovation, Systems and Technologies, 318. Springer. https://doi.org/10.1007/978-981-19-6347-6_29 DOI: https://doi.org/10.1007/978-981-19-6347-6_29
Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., [...] y Schönlieb, C. B. (2021). Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nature Machine Intelligence, 3(3), 199-217. https://doi.org/10.1038/s42256-021-00307-0 DOI: https://doi.org/10.1038/s42256-021-00307-0
Romero A. (2021). Understanding GPT-3 in 5 minutes, Medium. bit.ly/4d0XceT
Rosé, C. P., Martínez-Maldonado, R., Hoppe, H. U., Luckin, R., Mavrikis, M., Porayska-Pomsta, K., [...] y Du Boulay, B. (Eds.). (2018). AIED. Part I . Springer.
Rozado, D. (2023). The Political Biases of ChatGP, Soc. Sci. 12, 148. https://doi.org/10.3390/socsci12030148 DOI: https://doi.org/10.3390/socsci12030148
Rozado, D. (2023b). Danger in the Machine: The Perils of Political and Demographic Biases Embedded in AI Systems. Manhattan Institute, 14/03/2023. bit.ly/3Wj2w82
Salminen, J., Almerekhi, H., Kamel, A.M., Jung, S.G., y Jansen, B.J. (2019). Online hate ratings vary by extremes: A statistical analysis. Confer. on human information interaction and retrieval, 213-217. https://doi.org/10.1145/3295750.3298954 DOI: https://doi.org/10.1145/3295750.3298954
Sambasivan, N., Arnesen, E., Hutchinson, B., Doshi, T. y Prabhakaran, V. (2021). Re-imagining algorithmic fairness in india and beyond. Proceed. of the 2021 ACM confer. on fairness, accountab., and transp., 315-328. https://doi.org/10.1145/3442188.3445896 DOI: https://doi.org/10.1145/3442188.3445896
Sapiezynski P., Kassarnig V. y Wilson C. (2017). Academic performance prediction in a gender-imbalanced environment (Data set). Boise State Univ. https://doi.org/10.18122/B20Q5R DOI: https://doi.org/10.18122/B20Q5R
Sclater N. (2016). Learning analytics in higher education: a review of UK and international. bit.ly/3xU3RZz DOI: https://doi.org/10.18608/jla.2016.31.3
Scassellati, B., Admoni, H. y Matarić, M. (2012). Robots for use in autism research. Ann. Rev. of biom. Eng., 14(1). 275-294. https://doi.org/10.1146/annurev-bioeng-071811-150036. DOI: https://doi.org/10.1146/annurev-bioeng-071811-150036
Schiff, D. (2021). Out of the laboratory and into the classroom. AI & society, 36(1), 331-348. https://doi.org/10.1007/s00146-020-01033-8 DOI: https://doi.org/10.1007/s00146-020-01033-8
Segovia-García, N. (2024). Optimización de la atención estudiantil: una revisión del uso de chatbots de IA en la educación superior. European Public y Social Innovation Review, 9, 1-20. https://doi.org/10.31637/epsir-2024-324 DOI: https://doi.org/10.31637/epsir-2024-369
Seyyed-Kalantari, L., Zhang, H., McDermott, M. B., Chen, I. Y. y Ghassemi, M. (2021). Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nature medicine, 27(12), 2176-2182. https://doi.org/10.1038/s41591-021-01595-0 DOI: https://doi.org/10.1038/s41591-021-01595-0
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K. y Hassabis, D. (2017). Mastering chess and shogi by self-play with a general reinforcement learning algorithm. http://arxiv.org/abs/1712.01815
Smirnov, I. (2018). Predicting PISA scores from students’ digital traces. Proc. Intern. AAAI Confer. on Web and Social Media, 12(1). https://doi.org/10.1609/icwsm.v12i1.14996 DOI: https://doi.org/10.1609/icwsm.v12i1.14996
Stevens, E., Dixon, D. R., Novack, M. N., Granpeesheh, D., Smith, T. y Linstead, E. (2019). Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning. International journal of medical informatics, 129, 29-36. https://doi.org/10.1016/j.ijmedinf.2019.05.006 DOI: https://doi.org/10.1016/j.ijmedinf.2019.05.006
Suresh, H. y Guttag, J. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. Equity and access in algorithms, mechanisms, and optimization. EAAMO. https://doi.org/10.1145/3465416.3483305 DOI: https://doi.org/10.1145/3465416.3483305
Teninbaum, G. H. (2021). Report on ExamSoft’s ExamID Feature (and a Method to Bypass It). Journ. of Robotics, AI, and Law, 4. http://dx.doi.org/10.2139/ssrn.3759931 DOI: https://doi.org/10.2139/ssrn.3759931
Tsai, Y. T., Wang, C. C., Peng, H. S., Huang, J. H. y Tsai, C. P. (2018). Construction of artificial intelligence mechanical laboratory with engineering education based on CDIO teaching strategies. Innovative Techn. and Learn. 81-87.
https://doi.org/10.1007/978-3-319-99737-7_8 DOI: https://doi.org/10.1007/978-3-319-99737-7_8
Universidad de Austin (2020). Tweet del Departamento de Ciencias de la Computación UT Austin. https://twitter.com/UTCompSci/estado/1333890167782957060
Watters A. (2023). Teaching machines. The MIT Press
Waters A. y Miikkulainen R. (2014). GRADE: machine learning support for graduate admissions. AI Magazine 35(1), 64. https://doi.org/10.1609/aimag.v35i1.2504 DOI: https://doi.org/10.1609/aimag.v35i1.2504
West, S.M., Whittaker, M., y Crawford, K. (2019). Discriminating systems. AI Now, 1-33. bit.ly/4cEun8t
Wolf, Z. B. (2023). AI can be racist, sexist and creepy. What should we do about it. bit.ly/4cJbn8I
Yang, J., DeVore, S., Hewagallage, D., Miller, P., Ryan, Q. X. y Stewart, J. (2020). Using machine learning to identify the most at-risk students in physics classes. Physical Review Physics Educ. Research, 16(2), 020130. https://doi.org/10.1103/PhysRevPhysEducRes.16.020130 DOI: https://doi.org/10.1103/PhysRevPhysEducRes.16.020130
Zack, T., Lehman, E., Suzgun, M., Rodriguez, J. A., Celi, L. A., Gichoya, J., Jurafsky, D., Szolovits, P., W. Bates, D., E. Abdulnour, R. E., Butte, A. J. y Alsentzer, E. (2023). Coding Inequity. medRxiv, 2023-07. https://doi.org/10.1101/2023.07.13.23292577 DOI: https://doi.org/10.1101/2023.07.13.23292577
Zeide, E. (2019). Artificial intelligence in higher education: Applications, promise and perils, and ethical questions. Educause Review, 54(3). https://ssrn.com/abstract=4320049
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