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Michel-Villarreal, R.; Vilalta-Perdomo, E.; Salinas-Navarro, D.E.; Thierry-Aguilera, R.; Gerardou, F.S. Generative AI for Higher Education. Encyclopedia. Available online: https://encyclopedia.pub/entry/54115 (accessed on 02 July 2024).
Michel-Villarreal R, Vilalta-Perdomo E, Salinas-Navarro DE, Thierry-Aguilera R, Gerardou FS. Generative AI for Higher Education. Encyclopedia. Available at: https://encyclopedia.pub/entry/54115. Accessed July 02, 2024.
Michel-Villarreal, Rosario, Eliseo Vilalta-Perdomo, David Ernesto Salinas-Navarro, Ricardo Thierry-Aguilera, Flor Silvestre Gerardou. "Generative AI for Higher Education" Encyclopedia, https://encyclopedia.pub/entry/54115 (accessed July 02, 2024).
Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D.E., Thierry-Aguilera, R., & Gerardou, F.S. (2024, January 19). Generative AI for Higher Education. In Encyclopedia. https://encyclopedia.pub/entry/54115
Michel-Villarreal, Rosario, et al. "Generative AI for Higher Education." Encyclopedia. Web. 19 January, 2024.
Generative AI for Higher Education
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This entry is adapted from the peer-reviewed paper 10.3390/educsci13090856

ChatGPT is revolutionizing the field of higher education by leveraging deep learning models to generate human-like content. However, its integration into academic settings raises concerns regarding academic integrity, plagiarism detection, and the potential impact on critical thinking skills. 

academic integrity chatbots generative artificial intelligence

1. Introduction

Generative AI (GenAI) can be defined as a “technology that (i) leverages deep learning models to (ii) generate human-like content (e.g., images, words) in response to (iii) complex and varied prompts (e.g., languages, instructions, questions)” [1] (p. 2). As generative Artificial Intelligence (AI) continues to evolve rapidly, in the next few years, it will drive innovation and improvements in higher education, but it will also create a myriad of new challenges [2][3]. Specifically, ChatGPT (Chat Generative Pre-Trained Transformer), a chatbot driven by GenAI, has been attracting headlines and has become the center of ongoing debate regarding the potential negative effects that it can have on teaching and learning. ChatGPT describes itself as a large language model trained to “generate humanlike text based on a given prompt or context. It can be used for a variety of natural language processing tasks, such as text completion, conversation generation, and language translation” [4] (p. 4). Given its advanced generative skills, one of the major concerns in higher education is that it can be used to reply to exam questions, write assignments and draft academic essays without being easily detected by current versions of anti-plagiarism software [5].
Responses from higher education institutions (HEIs) to this emerging threat to academic integrity have been varied and fragmented, ranging from those that have rushed to implement full bans on the use of ChatGPT [6][7] to others who have started to embrace it by publishing student guidance on how to engage with AI effectively and ethically [8]. Nevertheless, most of the information provided by higher education institutions (HEIs) to students so far has been unclear or lacking in detail regarding the specific circumstances in which the use of ChatGPT is allowed or considered acceptable [9][10]. However, what is evident is that most HEIs are currently in the process of reviewing their policies around the use of ChatGPT and its implications for academic integrity.
Meanwhile, a growing body of literature has started to document the potential challenges and opportunities posed by ChatGPT. Among the key issues with the use of ChatGPT in education, accuracy, reliability, and plagiarism are regularly cited [1][3]. Issues related to accuracy and reliability include relying on biased data (i.e., the limited scope of data used to train ChatGPT), having limited up-to-date knowledge (i.e., training stopped in 2021), and generating incorrect/fake information (e.g., providing fictitious references) [11]. It is also argued that the risk of overreliance on ChatGPT could negatively impact students’ critical thinking and problem-solving skills [3]. Regarding plagiarism, evidence suggests that essays generated by ChatGPT can bypass conventional plagiarism detectors [11]. ChatGPT can also successfully pass graduate-level exams, which could potentially make some types of assessments obsolete [1].
ChatGPT can also be used to enhance education, provided that its limitations (as discussed in the previous paragraph) are recognized. For instance, ChatGPT can be used as a tool to generate answers to theory-based questions and generate initial ideas for essays [3][12], but students should be mindful of the need to examine the credibility of generated responses. Given its advanced conversational skills, ChatGPT can also provide formative feedback on essays and become a tutoring system by stimulating critical thinking and debates among students [13]. The language editing and translation skills of ChatGPT can also contribute towards increased equity in education by somewhat leveling the playing field for students from non-English speaking backgrounds [1]. ChatGPT can also be a valuable tool for educators as it can help in creating lesson plans for specific courses, developing customized resources and learning activities (i.e., personalized learning support), carrying out assessment and evaluation, and supporting the writing process of research [14]. ChatGPT might also be used to enrich a reflective teaching practice by testing existing assessment methods to validate their scope, design, and capabilities beyond the possible use of GenAI, challenging academics to develop AI-proof assessments as a result and contributing to the authentic assessment of students’ learning achievements [15].

2. Thing Ethnography Applied to ChatGPT

Ethnography refers to a form of social research that emphasizes the importance of studying first-hand what people do and say in particular contexts [16]. It involves an in-depth understanding of the world based on social relations and everyday practices. Traditionally, the focus of ethnography has been on human perspectives via qualitative methods such as observation and interviews. However, it is argued that as humans, “we have complex and intertwined relationships with the objects around us. We shape objects; and objects shape and transform our practices and us in return” [17] (p. 235). Recognizing this continual interplay between humans and objects necessitates research methodologies that grant both parties an equal role.
Recently, there has been a growing interest in moving away from a human-centered ethnographic approach to that of seeking nonhuman perspectives in a context where human perspectives are felt to be partial to fully understanding the interdependent relations between humans and nonhumans [18]. By studying things as incorporated into practices, we learn about both people and objects at the same time. This approach provides the opportunity to reflect on us by reflecting on things [19]. Relatively consolidated methods for exploring the distinct viewpoints, development paths, and possible worldviews of nonhuman entities incorporate ‘thing ethnography’ [20]. Thing ethnography is an approach that allows access to and interpretation of things’ perspectives, enabling the acquisition of novel insights into their socio-material networks. Thing ethnography emerges at the intersection of data that things give access to and the analysis and interpretation that human researchers contribute [21]. Giaccardi et al. [17] contend that adopting a thing perspective can offer distinct revelations regarding the interplay between objects and human practices, leading to novel approaches for collectively framing and resolving problems alongside these entities.
In this shift towards thing ethnography, artificial intelligence (AI) plays a significant role as its unique capacities provide unprecedented access to nonhuman perspectives of the world [18]. The unique perspectives of mundane things such as kettles, cups, and scooters can now be accessed via the use of software and sensors [19][21]. For instance, by attaching autographers to kettles, fridges, and cups, a study collected more than 3000 photographs that helped to uncover use patterns and home practices around these objects [19]. Another study looking into the design of thoughtful forms of smart mobility used cameras and sensors to collect data from scooters in Taipei, generating a better understanding of the socio-material networks among scooters and scooterists in Taiwan [21]. Moreover, the development of chatbots opens the possibility of directly interacting and accessing AI systems’ views via text exchange.
It has already been recognized that AI “has the potential to impact our lives and our world as no other technology has done before” [22], both positively and negatively. This knowledge is raising many questions concerning its ethical, legal, and socio-economic effects, and even calls for a pause in the development of more advanced AI systems [23]. With the increasing intelligence of things, it becomes crucial to adopt suitable perspectives to access, observe and understand the diverse social consequences and emerging possibilities that arise from this advancement [21]. So far, the relationship between humans and things in research has been unidirectional [17]. So, what happens if we change the focus to ‘things’, especially ‘things’ with human-like intelligence with the potential to become fully self-aware within the next few decades or even achieve Artificial General Intelligence (AGI) [24][25]? What happens if we try to understand the world from the perspective of a ‘thing’, such as AI, which is increasingly impacting many aspects of life? Applying thing ethnography to the study of AI systems can aid in exploring their social and cultural dimensions and their impact on society from their own perspective.
ChatGPT should not be studied merely as an “object” designed, trained, and used by people but as a “subject” that co-performs daily practices with users and has an impact on the socio-material networks in which it is embedded. In the case of thing ethnography applied to ChatGPT, the focus would be on understanding the user experience, the impact on communication dynamics, and the broader societal implications from ChatGPT’s perspective. A better understanding of this can help guide the development and deployment of chatbot technologies in a way that is more responsive to user needs, respects societal values, and promotes responsible AI practices. We believe that thing ethnography approaches are becoming crucially relevant as efforts attempting to build successively more powerful AI systems closer to AGI—AI systems that are generally smarter than humans—continue [25]. As ‘things’ become increasingly intelligent, there is an unprecedented and pressing demand to enhance our ability to access and comprehend the perspective of these entities.
Whereas previous thing ethnography interventions have used different smart devices to access the perspective of things, our study will engage with the ‘subject’ via a written conversation given the unique text generation capacities of chatbots. To answer the research questions stated in the previous section, this study has adopted a semi-structured interview. This type of interview facilitates the collection of rich data related to participants’ views [26], with the required degree of structure and flexibility [27]. An interview guide containing a list of themes and key questions was prepared before the interview. Nevertheless, the flexibility of semi-structured interviewing allowed the use of probes devised during the interviews. Probing is useful to further explore responses that are deemed significant for the research topic [26]. In line with King and Horrocks’ [28] suggestion, probing was adopted to seek clarification for unclear words or phrases, completion for stories that the interviewer felt unfinished, and elaboration to encourage the interviewee to ‘keep talking’.
The six-phase thematic analysis approach proposed by Braun and Clarke [29] formed the foundation for analyzing data from the semi-structured interview. This approach provided guidance for coding and categorizing the data, enabling a systematic, thorough, and cumulative analysis of the case study information. During the reporting phase, we aimed for dependability and transparency by providing verbatim quotes linked to specific codes being analyzed. These links are what connect raw data to “the data summary and interpretation generated by the researcher” [30] (p. 95). Additionally, we addressed transferability by comparing the emerging findings with the existing body of literature [31]. This was accomplished by examining similarities, contradictions, and their underlying reasons. This approach can lead to broader applicability and increased credibility of qualitative data.

3. ChatGPT's Perspectives on AI Integration in Higher Education: Opportunities, Challenges, Barriers, and Mitigation Strategies

The results align with previous studies, indicating the transformative potential of ChatGPT in education, yet highlighting significant challenges that must be addressed. The interview revealed key themes, including opportunities, challenges, barriers, and mitigation strategies.

Opportunities identified by ChatGPT include personalized feedback for students, supplementary resources, language and communication skill enhancement, accessibility improvements, and innovative learning experiences. Additionally, ChatGPT sees potential benefits for teaching and research staff, such as assisting with routine queries and supporting research efforts.

Challenges identified by ChatGPT revolve around quality control, expertise limitations, personalized learning challenges, and communication shortcomings. Notably, concerns arise regarding potential misuse by students, particularly in academic integrity issues like plagiarism and cheating.

The barriers to integrating GenAI technology in higher education, as identified by ChatGPT, include lack of awareness, technological constraints, resistance to change, ethical and privacy concerns, academic rigor, resource constraints, legal considerations, and lack of interdisciplinary collaboration.

Mitigation strategies proposed by ChatGPT involve policy development, education and training, collaboration efforts, research and development, ethical review processes, and continuous monitoring and evaluation. Emphasis is placed on addressing issues such as academic integrity, data privacy, algorithmic bias, and ethical considerations.

Results highlight the urgency of developing or updating academic policies, investing in education and training, engaging in research and development, and reevaluating traditional assessment methods to effectively integrate GenAI in higher education while mitigating associated challenges.

4. Conclusions

Employing a "thing ethnography" approach, the study explores ChatGPT's perspectives innovatively. This methodological framework tests biases, accuracy, and unveils insights not extensively documented. ChatGPT's transparency and ability to recall during interviews are noted benefits, though limitations include scope, replicability concerns, and potential research bias. The study encourages the use of thing ethnography, treating ChatGPT as an active subject influencing communication dynamics. It suggests ongoing conversations with ChatGPT for replicability and exploration of unaddressed topics, emphasizing the relevance of such approaches in the evolving landscape of advanced AI systems.

References

  1. Lim, W.M.; Gunasekara, A.; Pallant, J.L.; Pallant, J.I.; Pechenkina, E. Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. Int. J. Manag. Educ. 2023, 21, 100790.
  2. Dwivedi, Y.K.; Kshetri, N.; Hughes, L.; Slade, E.L.; Jeyaraj, A.; Kar, A.K.; Baabdullah, A.M.; Koohang, A.; Raghavan, V.; Ahuja, M.; et al. “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int. J. Inf. Manag. 2023, 71, 102642.
  3. 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.
  4. Baidoo-Anu, D.; Owusu Ansah, L. Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. SSRN 2023.
  5. Zhai, X. ChatGPT user experience: Implications for education. SSRN 2022.
  6. Reuters. Top French University Bans Use of ChatGPT to Prevent Plagiarism. Available online: https://www.reuters.com/technology/top-french-university-bans-use-chatgpt-prevent-plagiarism-2023-01-27/ (accessed on 28 April 2023).
  7. TheTab. These Are the Russell Group Unis that Have Banned Students from Using ChatGPT. Available online: https://thetab.com/uk/2023/03/03/these-are-the-russell-group-unis-that-have-banned-students-from-using-chatgpt-297148 (accessed on 28 April 2023).
  8. UCL. Engaging with AI in Your Education and Assessment. Available online: https://www.ucl.ac.uk/students/exams-and-assessments/assessment-success-guide/engaging-ai-your-education-and-assessment (accessed on 28 April 2023).
  9. University of Cambridge. Plagiarism and Academic Misconduct. Available online: https://www.plagiarism.admin.cam.ac.uk/what-academic-misconduct/artificial-intelligence (accessed on 28 April 2023).
  10. University of Oxford. Unauthorised Use of AI in Exams and Assessment. Available online: https://academic.admin.ox.ac.uk/article/unauthorised-use-of-ai-in-exams-and-assessment (accessed on 28 April 2023).
  11. Lo, C.K. What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature. Educ. Sci. 2023, 13, 410.
  12. AlAfnan, M.A.; Dishari, S.; Jovic, M.; Lomidze, K. Chatgpt as an educational tool: Opportunities, challenges, and recommendations for communication, business writing, and composition courses. J. Artif. Intell. Technol. 2023, 3, 60–68.
  13. Farrokhnia, M.; Banihashem, S.K.; Noroozi, O.; Wals, A. A SWOT analysis of ChatGPT: Implications for educational practice and research. Innov. Educ. Teach. Int. 2023, 1–15.
  14. Rahman, M.M.; Watanobe, Y. ChatGPT for education and research: Opportunities, threats, and strategies. Appl. Sci. 2023, 13, 5783.
  15. Wiggins, G. A True Test: Toward More Authentic and Equitable Assessment. Phi Delta Kappan 2011, 92, 81–93.
  16. Hammersley, M. Ethnography: Problems and prospects. Ethnogr. Educ. 2006, 1, 3–14.
  17. Giaccardi, E.; Speed, C.; Cila, N.; Caldwell, M.L. Things as co-ethnographers: Implications of a thing perspective for design and anthropology. In Design Anthropological Futures; Smith, R.C., Vangkilde, K.T., Otto, T., Kjaersgaard, M.G., Halse, J., Binder, T., Eds.; Routledge: London, UK, 2020; pp. 235–248.
  18. Reddy, A.; Kocaballi, A.B.; Nicenboim, I.; Søndergaard, M.L.J.; Lupetti, M.L.; Key, C.; Speed, C.; Lockton, D.; Giaccardi, E.; Grommé, F.; et al. Making Everyday Things Talk: Speculative Conversations into the Future of Voice Interfaces at Home. In Proceedings of the Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan, 8 May 2021.
  19. Cila, N.; Giaccardi, E.; Tynan-O’Mahony, F.; Speed, C.; Caldwell, M. Thing-Centered Narratives: A study of object personas. In Proceedings of the Research Network for Design Anthropology Seminar 3: Collaborative Formation of Issues, Aarhus, Denmark, 22 January 2015.
  20. Nicenboim, I.; Giaccardi, E.; Søndergaard, M.L.J.; Reddy, A.V.; Strengers, Y.; Pierce, J.; Redström, J. More-than-human design and AI: In Conversation with Agents. In Proceedings of the Companion Publication of the 2020 ACM Designing Interactive Systems Conference, Eindhoven, The Netherlands, 6 July 2020.
  21. Chang, W.W.; Giaccardi, E.; Chen, L.L.; Liang, R.H. “Interview with Things” A First-thing Perspective to Understand the Scooter’s Everyday Socio-material Network in Taiwan. In Proceedings of the 2017 Conference on Designing Interactive Systems, Edinburgh, UK, 10 June 2017.
  22. Dignum, V. Responsible Artificial Intelligence: Recommendations and Lessons Learned. In Responsible AI in Africa: Challenges and Opportunities; Eke, D.O., Wakunuma, K., Akintoye, S., Eds.; Springer Nature: Cham, Switzerland, 2023; pp. 195–214.
  23. Future of Life Institute. Pause Giant AI Experiments: An Open Letter. Available online: https://futureoflife.org/open-letter/pause-giant-ai-experiments/ (accessed on 22 April 2023).
  24. Gonzalez-Jimenez, H. Taking the fiction out of science fiction:(Self-aware) robots and what they mean for society, retailers and marketers. Futures 2018, 98, 49–56.
  25. OpenAI (2023) Planning for AGI and Beyond. Available online: https://openai.com/blog/planning-for-agi-and-beyond (accessed on 30 April 2023).
  26. Saunders, M.; Lewis, P.; Thornhill, A. Research Methods for Business Students, 8th ed.; Pearson Education: Harlow, UK, 2016.
  27. Merriam, S.B. Qualitative Research and Case Study Applications in Education; Jossey-Bass Publishers: San Francisco, CA, USA, 1998; ISBN 978-0787910099.
  28. King, N.; Horrocks, C. Interviews in Qualitative Research; Sage: London, UK, 2010; ISBN 978-1412912570.
  29. Braun, V.; Clarke, V. Using Thematic Analysis in Psychology. Qual. Res. Psychol. 2006, 3, 77–101.
  30. Guest, G.; MacQueen, K.M.; Namey, E.E. Applied Thematic Analysis; Sage Publications: Thousand Oaks, CA, USA, 2012; ISBN 978141297167.
  31. Eisenhardt, K.M. Building Theories from Case Study Research. Acad. Manag. Rev. 1989, 14, 532–550.
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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 :
Rosario Michel-Villarreal
,
Eliseo Vilalta-Perdomo
,
David Ernesto Salinas-Navarro
,
Ricardo Thierry-Aguilera
,
Flor Silvestre Gerardou
View Times: 332
Revision: 1 time (View History)
Update Date: 19 Jan 2024
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