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López-López, E.M.; Bru-Cordero, O.E.; Correa-Álvarez, C.D. Discipline-Sensitive Generative AI in Higher Education. Encyclopedia. Available online: https://encyclopedia.pub/entry/59773 (accessed on 25 June 2026).
López-López EM, Bru-Cordero OE, Correa-Álvarez CD. Discipline-Sensitive Generative AI in Higher Education. Encyclopedia. Available at: https://encyclopedia.pub/entry/59773. Accessed June 25, 2026.
López-López, Erika María, Osnamir Elias Bru-Cordero, Cristian David Correa-Álvarez. "Discipline-Sensitive Generative AI in Higher Education" Encyclopedia, https://encyclopedia.pub/entry/59773 (accessed June 25, 2026).
López-López, E.M., Bru-Cordero, O.E., & Correa-Álvarez, C.D. (2026, June 03). Discipline-Sensitive Generative AI in Higher Education. In Encyclopedia. https://encyclopedia.pub/entry/59773
López-López, Erika María, et al. "Discipline-Sensitive Generative AI in Higher Education." Encyclopedia. Web. 03 June, 2026.
Peer Reviewed
Discipline-Sensitive Generative AI in Higher Education
This is a peer-reviewed entry published in Encyclopedia Journal, full entry can be found at 10.3390/encyclopedia6060119

Generative artificial intelligence in higher education refers here to the use of computational systems that produce text, code, explanations, feedback-like responses, images, and other outputs from user prompts in university learning, coursework, assessment, and student study practices. This entry focuses on how students use generative AI while studying, preparing assignments, seeking explanations, revising work, programming, brainstorming, or responding to assessment tasks. It defines such use as a situated educational practice shaped by disciplinary expectations, assessment design, AI literacy, study habits, and academic integrity norms. From this perspective, the same AI-supported action may be acceptable as learning support in one course, ambiguous in another, and inappropriate when it conceals authorship, fabricates evidence, or substitutes for independent academic performance.

generative artificial intelligence higher education academic integrity disciplinary cultures student learning educational technology
Generative artificial intelligence has become part of ordinary student work in universities. Students use conversational systems and related tools to ask for explanations, revise drafts, test code, translate passages, plan study sessions, and prepare for assessments. They also use these systems to brainstorm, compare examples, receive feedback-like responses, and rehearse explanations before class [1]. These practices became highly visible after the public release of large language models, but the educational question is broader than any particular product. Universities now face a new form of academic mediation: a student may arrive at a sentence, answer, study plan, data summary, or line of code through interaction with a system that can sound helpful while lacking the accountability of a teacher, peer, author, or disciplinary expert [2][3].
Careful accounts of this issue avoid both enthusiasm and alarm. Generative AI can support learning when it helps students rehearse concepts, compare explanations, receive formative feedback, or examine alternative approaches. It can also weaken learning when it replaces reading, practice, disciplinary reasoning, or responsible authorship [4][5]. The same tool may be useful in one assignment and inappropriate in another. A student who asks for a simpler explanation before class is engaged in a different practice from a student who submits a generated answer in a closed assessment. Such distinctions are often clear in principle but difficult to communicate and enforce in everyday teaching.
Disciplinary culture is central to these distinctions. Academic fields differ in the kinds of knowledge they value, the forms of evidence they accept, and the ways students are expected to demonstrate learning. Biglan [6] classified academic areas along dimensions such as consensus, application, and concern with life systems; later work by Becher [7] and Neumann et al. [8] connected such differences to disciplinary cultures and teaching practices. Generative AI interacts with these differences. In literary studies, it may affect interpretation, voice, and citation practice. In computer science, it may generate code or debugging advice. In health sciences, it raises questions about accuracy, professional responsibility, and risk. In design, it touches ideation, authorship, and creative ownership.
Recent studies have begun to document variation in student and faculty responses to generative AI. Qu et al. [9] examine disciplinary differences in undergraduate engagement, while other studies report variation across institutional settings, assessment arrangements, and user groups [10][11]. Yet many policy discussions still treat students as a single population and AI use as a single behavior. That framing can lead to rules that are too broad to guide practice or too narrow to fit legitimate academic tasks. A more useful approach connects AI use to disciplinary expectations, study habits, self-regulated learning, assessment design, and academic integrity.
Existing studies already call for discipline-specific AI guidance in higher education [3][12]. The discipline-sensitive perspective developed here advances that discussion by linking three questions that are often separated: what students do with AI, how disciplines define legitimate academic work, and how institutions should design policy and assessment [13]. The framework therefore does not claim that disciplinary variation is new. Its contribution is to integrate disciplinary culture, student study practices, AI literacy, academic integrity judgments, and assessment design into one pedagogical account.
This entry offers a focused pedagogical synthesis of established knowledge on student use of generative AI in higher education. Its scope is student learning and course-based academic work: study practices, assessment tasks, academic integrity judgments, AI literacy, and the disciplinary expectations that shape legitimate assistance. The discussion gives particular attention to humanities and social sciences, engineering and computer science, health sciences, business and applied fields, and creative or design fields, because these domains illustrate different relationships among evidence, authorship, problem solving, professional judgment, and creative production. Policy issues are limited to educational guidance that directly affects students and courses: disclosure, authorship, permitted and prohibited assistance, assessment design, and program-level interpretation of institutional rules. The entry does not claim to cover every institutional use of AI. It focuses on how students use generative AI and how higher education can guide that use in ways that protect learning, fairness, and accountability. Additional information on the source base and synthesis procedure is provided in Supplementary Materials Text S1.

References

  1. Kasneci, E.; Sessler, K.; Küchemann, S.; Bannert, M.; Dementieva, D.; Fischer, F.; Gasser, U.; Groh, G.; Günnemann, S.; Hüllermeier, E.; et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learn. Individ. Differ. 2023, 103, 102274.
  2. Tlili, A.; Shehata, B.; Adarkwah, M.A.; Bozkurt, A.; Hickey, D.T.; Huang, R.; Agyemang, B. What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learn. Environ. 2023, 10, 15.
  3. Lo, C.K. What is the impact of ChatGPT on education? A rapid review of the literature. Educ. Sci. 2023, 13, 410.
  4. Farrokhnia, M.; Banihashem, S.K.; Noroozi, O.; Wals, A. A SWOT analysis of ChatGPT: Implications for educational practice and research. Innov. Educ. Teach. Int. 2024, 61, 460–474.
  5. Rahman, M.M.; Watanobe, Y. ChatGPT for education and research: Opportunities, threats, and strategies. Appl. Sci. 2023, 13, 5783.
  6. Biglan, A. The characteristics of subject matter in different academic areas. J. Appl. Psychol. 1973, 57, 195–203.
  7. Becher, T. The significance of disciplinary differences. Stud. High. Educ. 1994, 19, 151–161.
  8. Neumann, R.; Parry, S.; Becher, T. Teaching and learning in their disciplinary contexts: A conceptual analysis. Stud. High. Educ. 2002, 27, 405–417.
  9. Qu, Y.; Tan, M.X.Y.; Wang, J. Disciplinary differences in undergraduate students’ engagement with generative artificial intelligence. Smart Learn. Environ. 2024, 11, 51.
  10. Chan, C.K.Y.; Hu, W. Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. Int. J. Educ. Technol. High. Educ. 2023, 20, 43.
  11. Obed, K.; Anangisye, W.A.L.; Sanga, P. Academic integrity considerations of using ChatGPT in assessment activities among university student teachers. Qual. Assur. Educ. 2025, 33, 305–320.
  12. Eaton, S.E. Postplagiarism: Transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology. Int. J. Educ. Integr. 2023, 19, 23.
  13. Urquhart, L.; Ngo, X.M. Changing EAP assessment practices in the age of generative artificial intelligence: The case of Scottish higher education institutions. J. Engl. Acad. Purp. 2026, 79, 101609.
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Subjects: Education & Educational Research
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : Erika María López-López , Osnamir Elias Bru-Cordero , Cristian David Correa-Álvarez
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Online Date: 03 Jun 2026
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