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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Lopes, A. L. (2026). Generative AI and the Future of Higher Education: A Global Vision of Opportunities and Challenges. Artificial Intelligence in Organizations: Transformative Technologies, Applications, and Impacts (pp. 241?253). WORLD SCIENTIFIC (EUROPE). https://doi.org/10.1142/9781800618213_0011
Exportar Referência (IEEE) 
A. L. Lopes,  "Generative AI and the Future of Higher Education: A Global Vision of Opportunities and Challenges", in Artificial Intelligence in Organizations: Transformative Technologies, Applications, and Impacts, WORLD SCIENTIFIC (EUROPE), 2026, pp. 241-253
Exportar BibTeX 
@incollection{lopes2026_1791884055457,
	author = "Lopes, A. L.",
	title = "Generative AI and the Future of Higher Education: A Global Vision of Opportunities and Challenges",
	chapter = "",
	booktitle = "Artificial Intelligence in Organizations: Transformative Technologies, Applications, and Impacts",
	year = "2026",
	volume = "",
	series = "",
	edition = "",
	pages = "241-241",
	publisher = "WORLD SCIENTIFIC (EUROPE)",
	address = "",
	url = "https://www.worldscientific.com/doi/epdf/10.1142/9781800618213_0011"
}
Exportar RIS 
TY  - CHAP
TI  - Generative AI and the Future of Higher Education: A Global Vision of Opportunities and Challenges
T2  - Artificial Intelligence in Organizations: Transformative Technologies, Applications, and Impacts
AU  - Lopes, A. L.
PY  - 2026
SP  - 241-253
SN  - 2979-0700
DO  - 10.1142/9781800618213_0011
UR  - https://www.worldscientific.com/doi/epdf/10.1142/9781800618213_0011
AB  - Generative artificial intelligence (GenAI) is rapidly reshaping higher education by automating many tasks historically used to develop and assess student competence, from drafting essays and summarizing literature to writing code and answering exam-style questions. This chapter examines GenAI’s implications for universities through a combined technical, pedagogical, and institutional lens. It first explains the foundations of large language models, emphasizing their probabilistic nature and the resulting limits, including hallucinations and unreliable factuality, which make verification and critical judgment essential academic skills. It then analyzes the opportunity–risk landscape for higher education: potential gains in productivity for educators, increased access to always-available tutoring and cognitive support for students, and new possibilities for ideation and feedback, alongside risks of dependency, erosion of cognitive effort, academic misconduct that is difficult to detect reliably, and privacy and security concerns. To move from reactive prohibition to principled integration, the chapter presents an intervention model implemented at Iscte – University Institute of Lisbon, combining structured GenAI literacy initiatives for faculty and a foundational course for students. The chapter concludes with institutional recommendations for assessment redesign, curriculum integration of AI literacy, and policy approaches that preserve academic integrity while enabling responsible experimentation, arguing that GenAI ultimately forces a re-clarification of what higher education values in learning.
ER  -