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Irish Journal of Technology Enhanced Learning
Vol 7, Issue 2
Special Issue: The Games People Play: Exploring Technology Enhanced
Learning Scholarship & Generative Artificial Intelligence
The Irish Journal of Technology Enhanced Learning Ireland is the journal of the Irish Learning Technology
Association, an Irish-based professional and scholarly society and membership organization. (CRO# 520231)
http://www.ilta.ie/ . This is an Open Access article distributed under the terms of the Creative Commons
Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0).
Can Generative Artificial Intelligence Write an
Academic Journal Article? Opportunities,
Challenges, and Implications
Hsiao-Ping Hsu *
Dublin City University
Abstract
This article offers an in-depth reflection on the author’s experiences with Generative
Artificial Intelligence (Gen AI), ChatGPT 4.0. The author started the journey from their
initial need for software for English proofreading and editing services to their interest in
exploring pre-service teachers’ application of Gen AI in lesson planning. Based on prompt
engineering techniques, an iterative three-stage manuscript generation process—
brainstorming, refinement, and writing—with ChatGPT is detailed. A short paper generated
by ChatGPT is presented. Although Gen AI is a valuable tool in providing insights and
assistance in research idea generation and design, academic writing, and English writing
learning, the author cautions that critical thinking plays a vital role in ensuring accuracy,
ethical considerations, and the preservation of rigorous scholarly standards. As Gen AI
emerges as a game-changer in academia and education, this article highlights the importance
of balancing its emerging capabilities with maintaining traditional academic and educational
values.
1. Introduction
I connected with Gen AI because of my constant need for English proofreading and editing.
After exploring ChatGPT 3.5, I was impressed with its proficiency in proofreading and
editing English text. As an academic for whom English is a second language, I often rely on
English proofreading and editing software, such as Grammarly, particularly for frequent
email communication. Before discovering ChatGPT, I had to spend significant time drafting,
refining, and revising emails to maintain professionalism, even with the help of Grammarly.
With the help of ChatGPT, it requires less effort to efficiently craft emails that do not contain
personal or sensitive information, although minor grammatical and readability issues may
persist.
Additionally, thanks to its conversational user interface, ChatGPT feels like a private tutor
with whom I can converse and ask further questions to learn vocabulary and phrasing to
enhance my English writing. This is in line with the suggested learning strategy by Shemesh
(2023). Building upon this foundation, my exploration of ChatGPT’s potential has expanded
* Corresponding author: hsiao-ping.hsu@dcu.ie
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into teacher education. To outline the structure of this article, I will first describe the topic I
utilised ChatGPT to develop a manuscript and the strategies I employed in prompting it.
Subsequently, I will present the short report generated by ChatGPT, followed by a critical
reflection on the process and its educational implications.
1.1 The Short Report Topic
The research topic I asked ChatGPT to generate concerns the investigation of pre-service
primary teachers’ perceptions of Gen AI in lesson planning, taking the impacts of gender,
academic level and daily use of Gen AI into account. The rationale for choosing this topic
stems directly from my professional placement supervision in the Bachelor of Education
programme. One of the recurring challenges they expressed during post-observation
discussions is the creation of lesson plans, especially within tight timeframes and in fast-
paced school environments. A lesson plan is essential for teachers to prepare, organise, and
conduct lessons, and also serves as a means of evaluating a teacher’s instructional skills (Liu
& Zou, 2014). Drafting lesson plans is a standard requirement in teacher education
programmes. However, constructing lesson plans is often seen as challenging and time-
intensive (Alanazi, 2019; Colaco & Antao, 2023). Although some studies have emphasised
the efficacy of technology-integrated approaches in assisting student teachers with lesson
planning, like Google Docs (Colaco & Antao, 2023) and mobile learning applications
(Susantini et al., 2022), there is also a proposal to use Gen AI for lesson planning (Bonner et
al., 2023). However, pre-service teachers’ practical use of Gen AI still awaits more empirical
research. Thus, I applied ChatGPT to bridge the gap because of my familiarity with it.
1.2 Prompting Strategy
ChatGPT 4.0 Plus (Open AI, 2023), the September 25 version, was utilised for generating the
short paper and proofreading this manuscript’s abstract, introduction, and critical reflection
sections. This paid version requires a monthly subscription of $20. The short paper
generation encompassed three stages: brainstorming, refinement, and writing. All prompts are
shown in the table 1. The techniques of prompt engineering were employed to ensure
accurate and logically consistent responses (Ali et al., 2023; Bozkurt & Sharma, 2023; L. S.
Lo, 2023). The prompt engineering technique refers to the three fundamental components:
content knowledge, critical thinking, and iterative design (Cain, 2023). The content
knowledge involved my expertise and experience in teacher education, quantitative research
methods and academic writing. Critical thinking skills were applied to evaluate the responses
generated by ChatGPT critically. Additionally, the short paper generation process features an
iterative design, meaning that its three stages are not linear but rather iterative. For example, I
realised the literature review section lacked an introduction paragraph while writing. To
address this, I turned to ChatGPT for suggestions during the brainstorming stage and asked it
to refine a few sentences during the refinement stage.
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Table 1: Prompt and explanation
Stage
Prompt
Brainstorming –
establish outline
• You are a professor with great expertise in qualitative,
quantitative and mixed-method research design and the
application AI in education.
• I want to conduct a mixed-method study to investigate pre-
service primary teachers’ perceptions of using Gen AI for lesson
planning, referring to their gender, levels in the teacher
education. programme and daily use of Gen AI for personal and
professional purposes.
• Could you suggest a theoretical framework for my research?
• Thank you. I like to use the technology acceptance model
(TAM) as the primary theoretical framework of this study.
• Sorry, I think you have forgotten my original focus on the
impacts of primary teachers’ gender, levels in the teacher
education programme and daily use of Gen AI on their
perceptions of using Gen AI for lesson planning.
Refinement –
specify details
• Could you be more specific about the Quantitative Analysis?
• I like to you to regenerate a response after taking the variable of
actual system from the theoretical framework.
• Could you suggest a questionnaire that measures pre-service
primary teachers’ perceptions of using Gen AI for lesson
planning?
• Could you generate five semi-structured questions for focus
group interview?
• I like you to rework section 3 by producing more choices and
then dividing the generated choices into personal and
professional/pedagogical categories.
• I’d love you to rework the Professional/Pedagogical Use area by
taking the possibility that pre-service teachers might have used
Gen AI to do college works (e.g., writing a reading reflection).
• I need a question that asks student teachers’ pre-existing use of
Gen AI for lesson planning.
Writing –
generate the
exact short
report
• Based on what we have discussed so far, could you write a 400-
word introduction for my research question.
• Could you provide references and reference list to back up the
introduction?
• Could you write an 800-word literature review with references
and a reference list?
• I do not need the section on Methodological Considerations in
AI Education Research. Instead, I hope you replace this section
with a review of the teachers’ application of Gen AI for lesson
planning.
• Thank you for your work. I like you to rework the literature
review by including the following four sections: Gen AI in
Education, Pre-service Teachers and Technological Adoption,
the impacts of gender, academic level, and daily technology use
Irish Journal of Technology Enhanced Learning
161
on teachers’ perceptions of using emerging technologies for
lesson planning.
• Could you write a 500-word methodology section for this study?
Table 1: Prompt and explanation (continued)
Stage
Prompt
Writing –
generate the
exact short
report
• Please be more specific within the word limit, by considering the
following settings: (1) A four-year primary teacher education
programme with a total of 1,600 students. Each year has 400
students. 40 students are male while the rest of the 360 students
are female; (2) the theoretical framework is TAM; (3) The
questionnaire should be done in 10 minutes; (4) The focus group
interview should be done in 20 minutes.
• Could you provide reference and reference list to back the
methodology section?
• Could write an 800-word section for expected findings and
related discussions?
• I think the response has forgotten my interest in relation to the
impacts of the level in the teacher education programme and pre-
service teachers’ personal and professional/pedagogical uses on
their perceptions of using AI for session planning.
• I am not very happy with the generated response for the section
on expected findings and related discussion. Let’s restart again.
2. AI Generated Short Report
This section reports on early-stage research pending approval of research ethics and
subsequent data collection. The paper’s title is ‘Gender and Academic Level as Moderators:
Pre-service Primary Teachers’ Perceptions of Generative Artificial Intelligence in Lesson
Planning and its Mediation by Personal and Professional Use.’ Please refer to the other file to
access the short report generated by ChatGPT.
2.1 Introduction
The incorporation of Generative Artificial Intelligence (Gen AI) into educational contexts,
notably within pre-service primary teacher education and lesson planning, presents a complex
and multifaceted exploration. This study tentatively seeks to explore and unpack these
complexities, encapsulated in the research question: To what extent, if any, do gender and
academic level moderate the relationship between pre-service primary teachers’ perceptions
of Gen AI and its potential application in lesson planning, and is this relationship mediated
by their personal and professional use of Gen AI?
Lesson planning is commonly recognised as a critical component of effective teaching,
intertwining content knowledge, pedagogical skill, and innovative strategies, often posing
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challenges to pre-service teachers (Koehler & Mishra, 2009). Gen AI, with its potential to
autonomously create content, may offer support to navigate these challenges, although it is
essential to acknowledge that the adoption and application of such technology is potentially
influenced by various psychological, socio-demographic, and pedagogical variables.
Gender and academic level have been suggested to influence technological engagement and
application among pre-service teachers. For instance, possible differences in approaches and
attitudes towards technology between male and female pre-service teachers have been noted
in some contexts (Vekiri, 2010). Similarly, academic levels might influence technological
self-efficacy and consequently, interactions and acceptance of emerging technologies within
educational settings (Tondeur et al., 2016).
As pre-service primary teachers traverse their formative educational journey, interactions and
engagement with technology potentially shape their future pedagogical beliefs and practices
(Tondeur et al., 2012). Therefore, examining their perceptions and applications of Gen AI
may offer insights into their current academic and technological experiences and potentially,
into future technology-enriched educational environments (Teo, 2009). Gaining insights into
the factors that influence these perceptions and applications may be crucial for supporting
their development within technological and pedagogical domains.
Informed by the Technology Acceptance Model (TAM), this study cautiously approaches the
understanding that perceived usefulness and ease of use are significant factors in technology
adoption (Davis, 1989). It aims to gently probe the relationship between the acceptance of
Gen AI and its application in lesson planning amidst the challenges often faced by pre-
service teachers, with a mindful consideration of the mediating and moderating effects of
personal/professional use, gender, and academic level.
Given this, the research seeks to explore: To what extent, if any, do gender and academic
level moderate the relationship between pre-service primary teachers’ perceptions of Gen AI
and its application in lesson planning, mediated by their personal and professional use of Gen
AI? The intention here is not only to explore these dynamics but to contribute to the ongoing
conversations about pre-service teacher preparedness and practices in applying Gen AI in
lesson planning, offering insights into their practices, beliefs, and future professional
contexts.
2.2 Literature Review
In the evolving educational landscape, understanding the technological behaviours of pre-
service teachers is becoming increasingly vital. This literature review delves into several
interconnected areas. We begin by examining gender and academic progression, investigating
how they might influence technology adoption patterns among student teachers. Following
this, the Technology Acceptance Model (TAM) is introduced, a renowned framework that
provides insights into the factors driving technology acceptance. From there, we navigate to
the realm of Gen AI, probing its application in both the personal and professional spheres of
pre-service teachers’ lives. Concluding the review, we contemplate the potential of Gen AI as
a tool to assist these educators in lesson planning. Through this exploration, the intention is to
elucidate the nuanced relationships pre-service teachers have with technology, with a
particular emphasis on Gen AI.
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2.2.1 Pre-service Teachers: Unraveling Gender and Academic Level in Technological
Adoption
Engaging with literature around pre-service teachers’ adoption of technology, a subtle
narrative regarding gender and academic level begins to surface. Previous explorations, such
as that by Vekiri (2010), slightly pull back the veil on gender disparities, revealing the
possible divergent trajectories in both attitudes and self-efficacy towards Information and
Communication Technology (ICT) among male and female pre-service teachers. Similarly,
as pre-service teachers traverse through academic levels, their technological interactions
might witness variations, suggesting that their year of study might potentially influence their
perceptions and uses of technology in educational contexts (Tondeur et al., 2016).
Nevertheless, these investigations only partially illuminate the broader tableau, indicating a
necessity for a deeper, nuanced exploration that accounts for these variables in the context of
Gen AI.
2.2.2 Anchoring Explorations in the Technology Acceptance Model (TAM)
Navigating the ocean of technological adoption research, the Technology Acceptance Model
(TAM) often serves as a vessel for researchers, exploring user experiences and engagements
with technology. Fundamental to TAM is the assertion that perceived usefulness and
perceived ease of use stand as central pillars influencing technology acceptance and usage
(Davis, 1989). However, applying TAM to pre-service teachers extends beyond mere
application, invoking questions about how these foundational principles might intertwine
with pre-service teachers’ experiences, motivations, and challenges in their technological and
pedagogical journeys (Teo, 2009). The potential symbiosis between perceived utility, ease of
use, and individual variables like gender and academic level opens avenues for meticulous
exploration.
2.2.3 Gen AI in Dualistic Contexts: Personal and Professional Spheres
The landscape of Gen AI use, especially within pre-service teachers’ experiences, extends
across two realms: personal and professional. It’s implied that their engagements with Gen
AI could be shaped by an amalgamation of intrinsic and extrinsic motivations (Wang et al.,
2020), which could be distinctly different in personal versus professional contexts. Their
personal use might be guided more by intrinsic motivations and interests, while professional
usage could be tethered more to perceived utility and extrinsic motivations, like curriculum
demands and pedagogical efficacy. Yet, this dichotomy is speculative at best and demands a
more deliberate investigation into its reality and repercussions in the context of lesson
planning.
2.2.4 Gen AI: A Tentative Bridge in Lesson Planning for Pre-service Teachers
Pre-service teachers, often navigating the tempestuous seas of lesson planning, might find an
ally in Gen AI. The potential of Gen AI in mitigating challenges inherent in lesson planning
is not fully unveiled, yet it is tempting to envisage its capacity to bridge gaps between
theoretical knowledge and practical application (Zawacki-Richter et al., 2019). While the
hypothetical benefits, such as automated content generation, personalised resource
suggestion, and augmented content creation, are attractive, the actual perceptual and practical
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dynamics among pre-service teachers remain in shadows. The complexity of their
psychological, pedagogical, and technological interactions with Gen AI, especially in the
context of their gender and academic progression, requires a more profound exploration,
deeply embedded in their lived experiences and perceptions.
In wrapping up this literature review, it’s evident that the intersection of technology and
pedagogy, particularly with the advent of Gen AI, is reshaping educational paradigms. Our
exploration underscores the nuances that gender and academic stages introduce to the
technology adoption patterns among pre-service teachers. The insights offered by the
Technology Acceptance Model (TAM) further contextualise these patterns, offering a
framework for understanding their technological inclinations. Gen AI stands out with its
potential in both personal and professional contexts, notably in the realm of lesson planning.
However, there’s a clear gap in fully comprehending its implications and applications. Future
research must delve deeper, ensuring that our understanding evolves in tandem with the
technological advancements, aligning them with the broader educational goals.
2.3 Methodology
2.3.1 Research Design
This study utilises a mixed-methods approach, employing a sequential explanatory design, to
meticulously investigate pre-service primary teachers’ perceptions and applications of Gen
AI in lesson planning. With the Technology Acceptance Model (TAM) as the theoretical
framework, perceived usefulness and perceived ease of use are pivotal variables.
2.3.2 Participants
Participants are pre-service teachers from a four-year primary teacher education programme
with 1,600 students (40 males and 360 females per year). Quantitative phase participants will
be stratified randomly selected, ensuring representative sampling across all academic years
and genders (n=200). For the qualitative phase, 20 participants will be purposively selected
based on their questionnaire responses to ensure diverse insights.
2.3.3 Instruments
2.3.3.1 Questionnaire. A 10-minute online questionnaire will collect data on
demographics, perceptions, and use of Gen AI, informed by TAM’s constructs. It
includes sections on perceived ease of use, perceived usefulness, and behavioural
intention to use Gen AI, measured through Likert-scale items, and sections querying
the frequency and type of Gen AI use. Reliability and validity will be established via
a pilot test and expert review.
2.3.3.2 Focus Group. Subsequent 20-minute focus group discussions will explore
deeper insights into participants’ perceptions and experiences, employing semi-
structured questions derived from quantitative results and the TAM framework,
focusing on practical and perceptual aspects of Gen AI use in lesson planning.
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2.3.4 Data Collection
2.3.4.1 Quantitative Phase. A stratified random sample of students will be invited via
email to participate in the online questionnaire, ensuring ethical adherence through
informed consent and maintaining anonymity.
2.3.4.2 Qualitative Phase. Participants for focus groups will be selected based on
interesting or divergent questionnaire responses to ensure varied perspectives. The
discussions will be conducted virtually, recorded, and transcribed, respecting ethical
protocols, and participants’ consent and confidentiality.
2.3.5 Data Analysis
2.3.5.1 Quantitative Analysis. For this research, we will conduct both mediation and
moderation analyses to discern the intricacies of the relationships among the
variables. Mediation analysis will delve into the mechanism of how pre-service
primary teachers’ perceptions of Gen AI (Independent Variable) influence its
application in lesson planning (Dependent Variable). We hypothesise that this
relationship is mediated by their personal and professional use of Gen AI (Mediator
Variable). Moderation analysis will scrutinise how gender and academic level
(Moderator Variable) influence the strength or direction of the relationship between
pre-service teachers’ perceptions (Independent Variable) and the application of Gen
AI (Dependent Variable). In a nuanced moderated mediation analysis, we’ll
investigate if the mediating effect of personal and professional use of Gen AI differs
across gender or academic levels. The hypotheses will test if, for instance, the
mediation effect is more pronounced for final-year students compared to first-year
students or differs between male and female pre-service teachers. Prior to in-depth
analyses, assumptions for mediation and moderation, such as linearity and absence of
multicollinearity, will be tested to ensure the robustness of the findings.
2.3.5.2 Qualitative Analysis. Data from focus group discussions will undergo
inductive thematic analysis, focusing on identifying themes that elucidate the
quantitative findings, specifically around the practical and perceptual aspects of Gen
AI use and its alignment with TAM’s constructs.
2.3.5.3 Ethical Considerations. Ethical diligence will be paramount, involving
obtaining informed consent, ensuring participant anonymity, and the secure storage
and handling of data. Ethical approval will be sought from the relevant institutional
ethics review board.
2.4 Possible results and implications
This study anticipates noticeable variances in the acceptance and use of Gen AI in lesson
planning among pre-service teachers, influenced by their academic progression and gender.
Male and female pre-service teachers might display distinct patterns of Gen AI adoption. The
depth of personal use of Gen AI is predicted to correlate with its integration in professional
settings, suggesting that familiarity might bolster confidence in its pedagogical applications.
A strong intrinsic connection is expected between the perceived usefulness and ease of Gen
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AI use and its intended implementation in lesson planning. Exploring these perceptions,
aligned with the Technology Acceptance Model (TAM), will provide insights into potential
facilitators or hindrances for its adoption. Ethical concerns and practical challenges might
arise, emphasising a tension between pragmatic AI adoption and traditional planning values.
Ultimately, this research aims to deepen understanding of Gen AI adoption among pre-
service teachers, offering valuable insights for educational stakeholders.
2.5 References
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Quarterly, 13(3), 319-340.
Koehler, M., & Mishra, P. (2009). What is technological pedagogical content knowledge
(TPACK)? Contemporary Issues in Technology and Teacher Education, 9(1), 60-70.
Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service
teachers. Computers & Education, 52(2), 302-312.
Tondeur, J., Aesaert, K., Pynoo, B., Braak, J., Fraeyman, N., & Erstad, O. (2016).
Developing a validated instrument to measure preservice teachers’ ICT competencies:
Meeting the demands of the 21st century. British Journal of Educational Technology,
48(2), 462-472.
Vekiri, I. (2010). Socioeconomic differences in elementary students’ ICT beliefs and out-of-
school experiences. Computers & Education, 54(1), 941-950.
3. Critical Reflection
3.1 Reflection on Process
The whole process took approximately twelve hours. Four challenges were identified. The
initial challenge involved consistently reminding ChatGPT of the primary research objectives
in the context of the hypothetical study. I examined the impacts of three variables on pre-
service teachers’ perceptions of using Gen AI for lesson planning: gender, academic level,
and personal and professional use of Gen AI. Essentially, the longer the conversation, the
more likely ChatGPT was to omit these three variables from earlier in the thread. The second
challenge pertained to inconsistency in ChatGPT’s responses. For instance, when ChatGPT
was requested to suggest a quantitative data analysis technique suitable for the hypothetical
study, it initially outlined relevant statistical variables based on the technology acceptance
model (Davis, 1989) and proposed five analytical steps: pre-processing, descriptive analysis,
inferential analysis, model testing, and interpretation. After I requested the removal of a
particular variable from the TAM framework, ChatGPT generated a new response. However,
the suggested analytical steps decreased from five to three—particularly omitting the critical
Interpretation step. It is reasonable to assume that the interpretation step should have
remained after removing a single variable from the TAM statistical model. The third
challenge has been widely documented: ChatGPT may produce correct-sounding but
logically incorrect results (Dwivedi et al., 2023; National Academic Integrity Network,
2023). Finally, concerning citations and references, ChatGPT may generate a response with
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in-text citations but does not necessarily include the citations in the references. This finding is
consistent with the finding made by Walters and Wilder (2023).
In terms of ChatGPT’s strengths, it was found that ChatGPT can recommend appropriate
research questions and suggest potential research design ideas after being prompted with a
few keywords (Sallam, 2023). It also excels at recommending data analysis techniques
(Rahimi & Talebi Bezmin Abadi, 2023). In other words, using Gen AI could benefit
researchers aiming to identify a viable research question and subsequently outline a fitting
research design (Rahman et al., 2023). ChatGPT’s feature for generating lengthy content has
been articulated in enhancing academic writing efficiency and quality (Huang & Tan, 2023).
In addition to its value in supporting academic writing productivity, ChatGPT allows for two-
way communication, simulating human interaction, for English learners as noted by Imran
and Almusharraf (2023). If ChatGPT generates confusing responses, users can ask for further
clarification or a simplified version. Compared to other online proofreading and editing tools,
such as Grammarly, despite their recent integration of Gen AI, it mainly serves as a tool to
identify errors and suggest corrections through one-way communication. Therefore, it is less
likely to allow users to ask further questions.
Recognising the aforementioned strengths and challenges, the approach to writing, especially
for complex tasks like journal articles, requires a more dynamic strategy. The next time I
have the opportunity to write a journal article with the assistance of Gen AI, I will create a
guiding map listing the major and minor headings required for each chapter, enabling a
visualisation of current progress. This strategy helps clarify my progress in the dialogue with
ChatGPT by referencing these headings as I transition between the brainstorming,
refinement, and writing stages. Thus, I will be able to identify areas that require further
attention. For instance, I should have consulted ChatGPT to refine the focus group interview
questions to align with the updated questionnaire items. I failed to recognise this issue until I
was deep into writing this manuscript. This experience underscores the challenges of relying
solely on Gen AI to craft a journal article. Writing a journal article is not just about writing; it
encompasses idea generation, research design (including data collection/analysis), and
literature search. While Gen AI can assist in idea production and offer recommendations for
research design and literature review (Sallam, 2023), it ultimately falls on us, the scholars, to
critically evaluate the results generated by Gen AI, as Rahman et al. (2023) suggested.
3.2 Reflection on Implications
The adoption of Gen AI, particularly ChatGPT, in academic and educational contexts,
demonstrates a significant shift across various facets, including knowledge, scholarship,
teaching, learning, and assessment. Integrating Gen AI into academia has a great potential to
democratise knowledge and scholarship by enabling non-native English-speaking academics
to participate in scholarly discourse predominantly conducted in English. This accessibility
fosters a more inclusive academic environment but concurrently introduces challenges
concerning authorship and the authenticity of ideas (Lund et al., 2023; Morocco-Clarke et al.,
2023). Academic communities must emphasise cultivating original thought and critical
analysis, fundamental tenets of scholarly work. Discussions regarding the potential for Gen
AI to aid the education sector, including teaching, learning, and assessment, were widespread
(Barros et al., 2023; Chan & Hu, 2023; C. K. Lo, 2023). However, over-reliance on it may
hinder students’ critical thinking (Michel-Villarreal et al., 2023) and raise ethical concerns
(Elkhatat, 2023). Thankfully, in Ireland, clear guidance has been issued by the (National
168
Academic Integrity Network, 2023), though periodic reviews may be necessary to
accommodate the evolving AI landscape. This proactive approach in Ireland reflects the
global academic community’s need to balance the transformative potential of Gen AI with its
inherent challenges.
Gen AI’s emergence offers benefits and challenges for scholars, especially non-native
English-speaking scholars. It significantly enhances efficiency in producing and refining
content and creating opportunities for English writing learning. While Gen AI assists in
research idea generation and design (Sallam, 2023), scholars must critically evaluate these
AI-generated outcomes, maintaining their essential role in scholarly work (Rahman et al.,
2023). Thus, the value of Gen AI in academia hinges on its utility and scholars’ critical
engagement with its responses. In conclusion, integrating Gen AI into academia and the
education sector is a double-edged sword. While it offers significant benefits regarding
efficiency and accessibility, it also challenges us to rethink and redefine the essence of
knowledge, scholarship, teaching, learning, and assessment in the Gen AI age. As we
navigate this new terrain, we must ensure that Gen AI is used responsibly, complementing
rather than supplanting the human elements of scholarship and education.
Irish Journal of Technology Enhanced Learning
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