The desire path: unleashing expectations, discussing apprehensions, and proposing a way forward for GAI use in higher education
| Date | 14 November 2024 |
| Pages | 110-131 |
| DOI | https://doi.org/10.1108/ILS-10-2023-0137 |
| Published date | 14 November 2024 |
| Author | Diana Koroleva,Nazir Jogezai |
The desire path: unleashing
expectations, discussing
apprehensions, and proposing a
way forward for GAI use
in higher education
Diana Koroleva and Nazir Jogezai
Institute of Education, National Research University Higher School of Economics,
Moscow, Russian Federation
Abstract
Purpose –The purpose of this study is to demonstrate the desire path of using GAI in higher education,
includingexpectations, apprehensions, and the way forward.
Design/methodology/approach –This qualitative study employs thematic analysis, scrutinizing
11 interviewswith innovative higher education faculty. The methodologysection details the utilization of GAI
(Chat GPT) for conducting thematic analysis on interviews, showcasing academics' practical application of
this technologyfor research purposes.
Findings –Stakeholders expect continuous improvement in technology, overdependence, advocate for
gradual adjustment, and emphasize context-dependent technology utilization. Concerns encompass issues
such as data reliability, ethical considerations, risksof undermining fundamental aspects, limitations in fully
replacinghuman involvement, and worries about personal responsibility.
Practical implications –Recommendations include flexible regulations, data-driven decision-
making, professional development, diverse stakeholder engagement, and promoting distributed
responsibility.
Originality/value –This study offers valuable insightsinto the use of GAI in higher education, formulating
policiesthat encourage innovation without hampering effectiveness.
Keywords Generative artificial intelligence, AIED, Prompt, Higher education, University faculty,
Social construction of technology, Desire path
Paper type Research paper
Introduction
Higher education has recently transformed due to job market shifts and the move to remote
learning driven by the COVID pandemic. Today, itfaces a new challenge, closely linked to
the rapid advancements in artificialintelligence (AI). According to the literature, AI tools are
The collective authors extend their heartfelt appreciation to the research intern Sofia Zvereva from the
Laboratory for Educational Innovation Research Laboratory for her valuable contributions in
preparing the data for analysis.
Funding: The article was prepared within the framework of the project ‘Mirror Laboratories’HSE
University.
Conflict of interest: The authors reported no conflict of interest.
ILS
126,1/2
110
Received10 October 2023
Revised16 July 2024
9 October 2024
Accepted11 Oc tober 2024
Informationand Learning
Sciences
Vol.126 No. 1/2, 2025
pp. 110-131
© Emerald Publishing Limited
2398-5348
DOI 10.1108/ILS-10-2023-0137
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2398-5348.htm
recognized as pivotal technologies that wield a significant impact on the evolution of
learning and teaching, with a focus on AI for learning analytics (from an institutional
perspective) and AI for learning tools (from a student standpoint) (Pelletier et al.,2022).
Despite the historical presence of AI use in higher education [1], the release of ChatGPT
versions 3 and 4 has marked a newera of generative artificial intelligence (GAI) by bringing
the concept of GAI to the public attention and sparking interest in its potential impact on
education (Farrokhnia et al.,2024). The applications of GAI are wide-ranging, including
image generation, natural language processing, and music generation (Lim et al., 2022). It
has had a truly genuine shockwaveinnovation in the field (Koroleva and Andreeva, 2024).
The situation characterized by intensive transformation is indeed atypical within the
context of classical education. On the one hand, universities are tasked with upholding
traditions. They are undeniably connected to government policies, fulfilling state mandates,
and thus, orient themselves based on top-down requirements, including those related to
innovation (Yudkevich, 2019). On the other hand, the scientific endeavors taking place
within university walls,including innovations, drive progress and mustalign with the current
spirit of the times (Fan et al., 2021). In this case, universities also act as the vanguards of
development, positioning them at the forefront of scientific advancement and necessitating
agile and critical responses.
The academic staff in higher education institutions (HEIs) hold a pivotalrole not only in
generating new knowledge through traditional scientific methodologies but also in
effectively implementing innovations within the educational process. Faculty staff
undoubtedly form a heterogeneouscollective, with each member exhibiting varied responses
to emerging innovations (Kallenberg, 2020). Nevertheless, akin to any innovationvanguard,
where acceptance or diffusion matters, it is the response of the university faculty that can
better predict the educationaluse of GAI. Their adoption of GAI can occur more rapidlyand
meaningfully than in other sectors. In this context, tracking this group can yield valuable
insights into the process of innovation, particularly where little is known about how
university faculty, as frontline implementersof innovation, respond to GAI.
The expectations surrounding the role of AI in education (AIED), coupled with the
apprehensions articulated by academic professionals and their proposed strategies for the
future in the domain of AI, can be perceived as the concept of desired paths (Macfarlane,
2012). This study aims to investigate GAI's integration into the educational landscape
comprehensively, focusing on the expectations, apprehensions, and way forward from the
perspective of university faculty. By GAI, we understandadvanced AI language models that
can perform a variety of tasks such as answeringand formulating questions, writing content,
and engaging in conversations. The outputof these models could be text, images, code, etc.
These models are developed by different organizations. Examples of these models include
ChatGPT,Claude, GigaChat, etc.
This study will help generate more relevant insights in this regard and pave the way for
the meaningful educational use of GAI in higher education. The results could be used by
stakeholders for the development of meso- and macro-level policies in the context of AIED.
This signifies that innovations will be allowed to flourish and receive support where
necessary, without succumbing to a rigid regulatory environment that may potentially
undermine their efficacy.
Literature review
AI has transformed to a greater extentand at a rapid pace. The inception of GAI represents a
dramatic advancement from previousAI models. It leverages deep learning models to create
content including audio, code, images, text, simulations, 3D objects, and videos that sound
Information and
Learning Sciences
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