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Kohnke, L., Zou, D., & Moorhouse, B. L. (2024). Technostress and English language teaching in the age of generative AI.
Educational Technology & Society, 27(2), 306-320. https://doi.org/10.30191/ETS.202404_27(2).TP02
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ISSN 1436-4522 (online) and 1176-3647 (print). DOI 10.30191/ETS. This article of Educational Technology & Society is available under Creative Commons CC-BY-
NC-ND 3.0 license (https://creativecommons.org/licenses/by-nc-nd/3.0/). For further queries, please contact Editors at ets.editors@gmail.com.
Technostress and English language teaching in the age of generative AI
Lucas Kohnke1, Di Zou2* and Benjamin L. Moorhouse3
1Department of English Language Education, The Education University of Hong Kong, Hong Kong, China //
2Centre for English and Additional Languages, Lingnan University, Hong Kong, China // 3Department of
Education Studies, Hong Kong Baptist University, Hong Kong, China // lucaskohnke@gmail.com,
lmakohnke@eduhk.hk // dizoudaisy@gmail.com, dizou@ln.edu.hk // blmoorhouse@hkbu.edu.hk
*Corresponding author
ABSTRACT: Technostress is a phenomenon in which rapid technological advancement affects teachers’
psychological well-being. It is an emerging concern in English language education, which may be exacerbated
by the advent of generative artificial intelligence (GenAI) tools such as ChatGPT. This study explores the factors
that influence technostress among English language teachers using GenAI tools and strategies that can alleviate
it. Based on the analysis of qualitative data from semi-structured interviews with 16 instructors at higher
education institutions in Hong Kong, the study identifies the rapid advancement of AI technology, inadequate
training and lack of experience as contributors to technostress. It also names mitigating strategies including
targeted professional development, online engagement and gradual integration. These techniques can foster
Technological Pedagogical Content Knowledge (TPACK) and reduce the challenges of incorporating GenAI into
English teaching. The findings align with existing literature on the impact of technostress and the role of
TPACK. The practical implications include the need for comprehensive training, supportive communities and a
balanced approach to AI integration. This investigation also expands the theoretical understanding of
technostress in English language teaching and the use of GenAI tools, providing empirical support for existing
frameworks. It also suggests directions for future research, which could investigate teacher well-being, effective
AI integration and the impact of TPACK.
Keywords: Technostress, Generative AI, Higher education, English language education, TPACK
1. Introduction
Artificial Intelligence (AI) is rapidly becoming an integral tool in education, which can foster personalized
learning, alleviate teacher workload and promote inclusive education (Chiu et al., 2022; Rivi et al., 2023). In
particular, the new wave of generative AI (GenAI) tools based on large language models (e.g., ChatGPT, DALL-
E2) have transformed language teaching and learning (Kohnke et al., 2023a; Tlili, 2023). These systems leverage
advanced algorithms and large-scale machine learning to produce original content, enrich existing material and
generate contextually relevant and individualized responses (Dwivedi et al., 2023; UNESCO, 2023). They can
also develop complex problem-solving scenarios, stimulate creative thinking and inspire a deeper understanding
of the subject matter among students (Lim et al., 2023). This is particularly evident in higher education, where
developments in GenAI have already affected teaching, learning and assessment practices in English language
teaching programmes (Bishop, 2023; Mohamed, 2023).
However, integrating GenAI tools into higher education is also accompanied by challenges. Despite their
significant potential to enhance learning experiences (Kohnke et al., 2023b), they can place a burden on
educators that is known as “technostress.” Technostress is defined as an adverse effect on an individual’s
attitude, psychology and behaviour due to the introduction or adoption of new or updated technology (Tarafdar et
al., 2011, 2019, 2020). It can lead to issues like depression, concentration problems, decreased job satisfaction
and reduced work performance (Jena, 2015; Salo et al., 2019).
When teachers adopt new technologies (Hodges & Ocak, 2023; McGrath et al., 2023), they are at risk of
experiencing technostress (Joo et al., 2016; Tarafdar et al., 2015). This danger is particularly acute in English
language teaching programmes. In recent years, there has been an increasing demand for English language
teachers to develop the skills and knowledge they need to use emergent forms of technology effectively
(Moorhouse & Kohnke, 2021). Moreover, AI-powered language models such as ChatGPT can significantly
impact language instruction, text creation and translation, which are especially relevant in English courses (Chiu
et al., 2023; Maddigan & Susnjak, 2023). This risk is compounded by the fact that many English teachers do not
fully understand AI, heightening their anxiety.
To address these challenges, this study aims to explore the factors that influence technostress among English
language teachers in an environment where using technology is mandatory, focusing on the adoption of GenAI.
The research questions that guide the investigation are as follows:
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RQ1: What factors contribute to technostress among English language teachers who use generative AI?
RQ2: What strategies effectively alleviate technostress among English language teachers integrating generative
AI tools into their teaching practices?
2. Literature review
2.1. Generative AI
Since the release of ChatGPT in late 2022, GenAI has become more accessible, cost-effective and efficient,
leading to a surge of interest in its use in language instruction. While some institutions have prohibited its use,
others have prudently accepted the emergence of GenAI (Tlili, 2023). They have adopted a forward-thinking
strategy, recognized that GenAI represents a significant evolution from the AI tools of the past and supported
students and teachers to use it effectively, ethically and transparently (Russel Group, 2023). Since July 2023,
several alternatives to ChatGPT have been launched, including Bard, Llama and Alpaca. They leverage deep
learning models to generate human-like content, including audio, code, images, text, simulations, 3D objects and
videos (UNESCO, 2023). As university students are frequent users of tablets, laptops and smartphones, GenAI
applications are convenient tools for them to adopt (Lai et al., 2023).
In the context of language learning, ChatGPT is highly valued for its capacity to stimulate authentic interactions,
thereby motivating learners to develop their language proficiency (Ali et al., 2023; Liu & Ma, 2023). Its
capabilities include answering students’ questions, extracting key ideas from articles, generating summaries,
offering practice questions and answers and providing feedback on grammar, coherence and writing style
(Kohnke et al., 2023a; Halaweh, 2023). Moreover, ChatGPT can help create a personalized, user-friendly and
result-oriented virtual learning environment that is tailored to each student’s needs (Annamalai et al., 2023;
Yilmaz & Yilmaz. 2023). GenAI tools can also develop interactive activities (Rose, 2023), recommend pertinent
resources including articles, videos and quizzes, and assist instructors with lesson preparation (Kohnke et al.,
2023b). Moreover, as GenAI advances, it promises to provide even more valuable resources for language
learning, making it an essential component of modern education (Wang et al., 2023).
However, some disadvantages of GenAI language tools have also been reported in the literature and are
particularly relevant for English language learners and teaching. These include issues related to interpersonal
connection, collaboration, problem-solving, critical thinking, self-efficacy and empathy (Ebadi & Amini, 2022).
These features are vital for learning the nuances of English and developing cultural understanding. Additionally,
GenAI tools can perpetuate existing biases in the language, leading to discrimination and stereotyping (Wei &
Niemi, 2023). This is a pertinent issue for English language learners, who come from diverse backgrounds and
whose understanding of the language is still developing. Moreover, AI tools provide limited exposure to
authentic language (Alam, 2022), as they often rely on pre-programmed datasets and algorithms. This can hinder
English language learners from attaining a comprehensive understanding of how English is used in real-life
contexts. Therefore, English language teachers need to carefully weigh these factors when deciding whether to
incorporate these tools to complement their teaching strategies.
2.2. Technostress among teachers
Brod (1984) first introduced the concept of technostress, describing it as “a modern disease of adaptation caused
by an inability to cope with the new computer technologies in a healthy manner” (p. 16). As technology has
continued to advance, technostress has garnered increasing interest among researchers, particularly those who
focus on the educational setting. Dong et al. (2020) characterize technostress as a current-day affliction among
teachers that impacts their ability to adjust and respond healthily to the increased use of innovative technologies
in education. Similarly, Estrada-Muoz et al. (2020) describe technostress as a condition related to age, workload
and the perceived work environment. From a psychological standpoint, it concerns the dynamic relationship
between an individual and his or her environment; people can have positive or negative attitudes towards a new
setting (Wang et al., 2020). Technostress can arise when individuals utilize technologies in various domains,
including work, education, communication and leisure activities (Wang et al., 2020; Zhao et al., 2022). Research
also suggests that adopting new technologies can blur the boundaries between leisure time and professional
responsibilities, as these aspects of life become intertwined (Khlaif et al., 2022; Salazar-Concha et al., 2021).
Because many language teachers do not understand AI, they are likely to experience anxiety and stress when
asked to use it (Kohnke et al., 2023b).
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Studies in the fields of medicine, economics and psychology have established a negative association between
technostress and various factors, including health, job performance, job satisfaction, commitment and
organizational productivity (Hwang & Cha, 2018; Suh & Lee, 2017; Tarafdar et al., 2011). Conversely, some
scholars have posited an association between technostress and positive outcomes, such as heightened efficiency
and innovation in work-related activities (Li & Wang, 2021; Tarafdar et al., 2019). This duality suggests the
importance of examining the implications of technostress for teachers who utilize GenAI and other emerging
technologies. This is especially crucial in consideration of the observation that GenAI is arguably the
technological tool that humans have adopted the fastest. Indeed, ChatGPT is the “fastest-growing consumer
application in history” (Gordon, 2023, p. 1). This disruption is likely to create a novel context for technostress.
2.3. Factors contributing to technostress in teachers
According to Tarafdar et al. (2020), there are five factors that cause technostress: techno-complexity, techno-
overload, techno-invasion, techno-insecurity and techno-uncertainty. First, techno-complexity emerges when
individuals must continually acquire new knowledge due to the intricate nature of technology. Second, techno-
overload arises when the pervasiveness of technology compels individuals to modify their habits and work faster.
Third, techno-invasion refers to the necessity of devoting personal time to staying current with technology and
connected to work. Fourth, techno-insecurity encompasses apprehension regarding the potential that new
technologies (or others with greater technological proficiency) will take one’s job. Finally, techno-uncertainty
pertains to uneasy feelings due to the frequent changes and updates to technology.
Other scholars have identified several additional factors that contribute to the degree of technostress teachers
experience. One is technological pedagogical content knowledge (TPACK) (e.g., Mishra & Koehler, 2006), a
framework that underscores the intersection of these three aspects of education and places the teacher at the core.
It emphasizes understanding how they interact and influence teaching practices (Zou et al., 2022). Teachers who
lack TPACK may experience technostress due to the perceived obligation to use technology (Dong et al., 2020).
This can manifest as difficulty managing technological resources or apprehension about keeping up with trends.
However, developing a strong foundation in TPACK can significantly reduce technostress: when teachers are
more adept at seamlessly integrating technology with pedagogy and content, they feel more confident, competent
and comfortable (Ö zgür, 2020). Therefore, enhancing TPACK can be an effective strategy for alleviating
technostress and learning how to utilize the full potential of technology to enrich learning experiences.
Another significant factor is self-efficacy: confidence in one’s ability to use technology effectively in the
classroom (Chou & Chou, 2021; Dong et al., 2020). Additionally, familiarity with technology plays a role, as
teachers who are more experienced with technology generally face fewer challenges when learning how to use
new tools (Upadhyaya & Virindi, 2020). Lastly, organizational culture and commitments can impact
technostress, as supportive environments inhabited by individuals who exhibit a clear commitment to technology
integration may facilitate smoother transitions and less stress (Camarena & Fusi, 2022).
Finally, demographic factors may play a part; however, researchers have reached varying conclusions regarding
their relationship with experiences of technostress. For instance, zgr (2020) reported that gender did not have
a significant influence on technostress, while Tarafdar et al. (2011) observed that men experienced higher levels
of technostress and oklar and Sahin (2011) found that it affected women more. Inconsistencies have also been
found regarding the impact of age. Similarly, a study by Maier et al. (2015) suggested that age had no significant
correlation with technostress, while Jena and Mahanti (2014) found that older individuals experienced higher
levels of technostress than their younger counterparts and other researchers reported that younger people
experience more technostress (Hsiao et al., 2016; Ragu-Nathan et al., 2008). Accordingly, the existing literature
on the influence of gender and age on technostress is inconclusive.
Acknowledging these factors is a crucial part of understanding the overall impact of technostress on teachers and
developing strategies to mitigate its adverse effects. This study focuses on university English language teachers,
who face unique challenges and consequences related to technostress.
2.4. Gaps in the literature
To summarize, technostress is a contemporary challenge faced by educators, which has grown more pronounced
with the proliferation of advanced technological tools, including GenAI tools such as ChatGPT. Investigating the
primary factors that contribute to technostress among English language teachers using such tools is paramount; it
can help gauge the psychological and professional implications of rapid technological change for educators who
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are at the frontline. In today’s rapidly digitizing educational landscape, however, it is not sufficient to merely
identify these stressors; it is equally imperative to delineate effective strategies to alleviate them. As teachers
strive to create optimal learning environments, their mental well-being and confidence directly influence their
efficacy. By pinpointing actionable strategies to combat technostress, it is possible to empower educators to
harness the full potential of GenAI tools without being overwhelmed. This will ensure that they consistently
deliver high-quality lessons and have a positive and sustainable professional experience.
Moreover, the literature on technostress highlights the complex dynamics of technology use, individual factors
and broader educational contexts, particularly in higher education. Previous studies have produced mixed or
inconclusive results regarding the influence of demographic factors, such as age and gender, on technostress.
Despite the growing body of literature on the topic, there is a need for further research exploring technostress
related to GenAI that identifies strategies to mitigate its adverse effects on English language teachers.
Technostress among English language teachers in relation to GenAI has not been studied extensively. Most
research has focused on technology in general (e.g., Dong et al., 2020; Joo et al., 2016; Ö zgür, 2020). This lack
of research presents an opportunity to explore ways to reduce its negative impacts. In addition, as previously
stated, language teachers may face specific concerns and challenges (e.g., learner motivation, relevance,
assessment design) due to the emergence of AI tools, which can directly affect their psychological well-being.
This study aims to address this gap by investigating university English language teachers’ experiences with
GenAI and considering how to manage technostress in higher education. By focusing on this novel aspect, the
study will contribute to the existing body of knowledge on technostress in education.
3. Methodology
Given the exploratory nature of this study, a qualitative approach was employed. The objective was to explore
English language teachers’ views and perceptions of technostress related to GenAI (Cohen et al., 2011). This
study was also guided by the interpretivist framework, which posits that knowledge and truth are constructed
rather than uncovered and this process is influenced by individuals’ perceptions of reality (Creswell, 2008).
Researchers within the tradition of interpretivism strive to comprehend and examine phenomena within their
contexts. As a result, the findings of this study represent the participants’ thoughts during the period of data
collection; they may evolve or change over time. Importantly, we did not aim for generalizability but to present
an accurate representation of the lived experiences and perceptions of the study’s participants.
3.1. Research context and participants
This research encompassed 16 instructors from the English language centres at four of the nine publicly funded
universities in Hong Kong. They offer undergraduate and postgraduate programmes in which English is the
medium of instruction (EMI). To help students adapt to the EMI environment, these universities require students
to take some courses through their English language centres. Therefore, these centres are pivotal in helping
students transition from secondary school to university life and adapt to a wholly English academic environment.
In May 2023, we read the staff profiles of the instructors at the various English language centres to identify
possible participants. We sent them email invitations to participate in the study, which included details about the
purpose and procedures. Sixteen of the instructors responded positively. The participants exhibited similar
educational backgrounds, holding either a master’s or doctoral degree in English language teaching. The group
consisted of eight male and eight female teachers. Their ages ranged from 33 to 51 years old. They had between
six and 22 years of teaching experience. All participants provided informed consent and were assigned
pseudonyms to maintain anonymity. Detailed profiles can be found in Table 1. We acknowledge that the
participants may not be representative of all English language instructors in Hong Kong, given the self-selected
nature of the sample; teachers who were interested in AI and language teaching were more likely to respond.
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Table 1. Participants’ demographic information
#
Pseudonym
Institution
Gender
Age
AI teaching experience
1
Jennifer
A
Female
49
3 years
2
Marge
A
Female
35
2 years
3
Anna
A
Female
37
2 years
4
Rob
A
Male
42
4 years
5
Mitchell
A
Male
50
3 years
6
Martin
A
Male
32
1 year
7
Emily
B
Female
34
1 year
8
Olivia
B
Female
38
3 years
9
Gabriel
B
Male
48
5 years
10
Ethan
B
Male
33
1 year
11
Chloe
C
Female
47
4 years
12
Alice
C
Female
40
2 years
13
Leo
C
Male
51
1 year
14
Charles
C
Male
42
2 years
15
Elizabeth
D
Female
51
1 year
16
Henry
D
Male
37
3 years
3.2. Data collection and analysis
Data were collected through semi-structured interviews conducted in June 2023 – either over Zoom or in person,
depending on the participant’s preference. We chose semi-structured interviews because they align with the
interpretivist paradigm. They allowed us to engage in a flexible and interactive exploration of the participants’
experiences, perceptions and opinions regarding technostress and the use of GenAI.
To ensure consistency, we provided the participants with the following definitions: “Generative artificial
intelligence (AI) describes algorithms (such as ChatGPT) that can be used to create new content, including
audio, code, images, text, simulations and videos” (McKinsey & Company, 2023, p. 1) and technostress refers to
the negative impacts, on an individual’s attitude, psychology and behaviour, of introducing or adopting new or
updated technology (Tarafdar et al., 2011; Tarafdar et al., 2019; Tarafdar et al., 2020). The interview guide,
developed for the study, comprised the following questions:
• Can you describe your experience integrating generative AI into your teaching practice?
• What aspects of generative AI have you found most challenging to manage in your teaching practice?
• Have you experienced any form of technostress when using generative AI in your teaching? If so, can you
provide examples of such experiences?
• What actions or strategies have you employed to alleviate or manage technostress related to the use of
generative AI in your teaching practice?
• In your opinion, what types of support or resources should higher education institutions provide to help
English language teachers manage the technostress associated with generative AI?
• How can higher education institutions encourage the development of digital literacy and facilitate
engagement with generative AI to minimize potential technostress among teachers?
Before the interviews, the questions were reviewed and discussed by the three researchers. The interviews were
conducted in English and ranged from 28 to 45 minutes. Each interview was audio-recorded and transcribed,
then subjected to inductive thematic analysis (Braun & Clarke, 2006). Participants were provided with copies of
the transcripts for an initial member check (Merriam & Tisdell, 2016). The manual coding process involved the
following steps:
• Reading the transcripts thoroughly to become familiar with the data.
• Independently generating initial codes, then sharing and comparing them using Google Docs; repeatedly
cross-checking the datasets and codes to ensure salience and consistency.
• Reviewing and organizing the emerging themes.
• Reaching a consensus on the themes and subthemes identified.
• Selecting representative extracts to illustrate the themes, ensuring that the data accurately reflected
participants’ perspectives.
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To ensure transparency and trustworthiness, a second member check was then conducted (Merriam & Tisdell,
2016). Each participant received a copy of the themes, subthemes and representative quotes identified. No
participants requested additions or provided further suggestions.
4. Findings
We have organized the findings in terms of three main themes that emerged during the analysis. Quotations from
the participants’ responses in the interviews are presented verbatim to ensure that their voices come through.
4.1 Theme 1: Causes and consequences of technostress among teachers
4.1.1. The challenges of integrating AI
The participants discussed two key interrelated factors that contribute to technostress: the speed with which AI
technologies are advancing and the difficulties of integrating them into their teaching practices. This is where
TPACK comes into play: underdeveloped TPACK can contribute to technostress, particularly when new
technologies such as AI are introduced. For example, Henry, a teacher with ten years of experience, expressed
the frustration and anxiety he experienced when adapting to the emergence of AI, stating:
I have little time to improve my IT abilities… I know AI can assist my students in generating ideas and
improving their writing, but I’m not sure how to use it effectively, and I’m stressed because the students
expect me to. (Henry)
The constant technical issues he faces each time he tries to use generative AI in class have left him feeling
demoralized. Moreover, he is unable to resolve these problems due to his lack of technological knowledge.
Similarly, Anna added:
I try to keep up because I know it can benefit my students, but it often means spending evenings and weekends
learning new features. This has impacted my work-life balance and caused stress in my relationship. (Anna)
These comments from Henry and Anna illustrate how the breakneck pace of AI and the relentless launch of
novel software applications create constant pressure to increase TPACK. Many participants also conveyed
apprehension concerning efficient integration, signifying a gap in their TPACK that compounds their
technostress. For example, Charles has been hesitant to integrate generative AI due to previous failed technology
initiatives in his classroom; the constant issues he experienced made him lose confidence in their value for his
students’ learning. This suggests that negative experiences can affect the development of TPACK and increase
technostress. By addressing gaps in their TPACK, teachers could alleviate some of this stress, increase their
confidence and prepare themselves to harness the potential of emerging technologies like AI.
4.1.2. Impact on teacher efficacy
Technostress also directly impacts how effectively teachers can perform their duties in the classroom, as well as
students’ performance. This interplay is also significantly influenced by TPACK. An inadequate understanding
of how to integrate technology can make teachers reluctant to adopt new tools and reduce engagement. Chloe,
for instance, avoids using AI tools because she lacks proficiency with them, indicating a gap in her TPACK. She
shared:
I just avoid using these new AI tools in my teaching because I don’t feel fully competent in using them in my
class. But my students seem less engaged when we stick to the old teaching methods. (Chloe)
Similarly, Marge expressed her fear of “looking stupid in front of students.” This feeling was echoed by Emily,
whose “lack of experience” made her “feel incompetent in her students’ eyes.” Elizabeth, who has more than 15
years of teaching experience but limited familiarity with generative AI, felt anxious about using new programs
due to concerns about “software bugs derailing lessons, which would reflect poorly on my competence” and lead
to “student disengagement.” This has caused her significant anxiety about receiving negative evaluations from
her students.
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Martin, an early career teacher with three years of experience, recounted a particular instance where he attempted
to use AI and failed:
Last week, I tried using this AI writing assistant thing in class, but it kept glitching and then just crashed. It
messed up the whole lesson flow, and we couldn’t even finish the writing exercise I had planned. (Martin)
This incident underlines how technical glitches, which cannot be resolved by teachers with underdeveloped
TPACK, can disrupt lessons, add to technostress and impact students’ learning experiences. Indeed, the need to
resolve technical issues in real time is a salient stressor, as reflected in the interview data. This exemplifies how
lower levels of TPACK can affect classroom management and the flow of instruction. In particular, teachers
expressed concerns about wasting class time resolving problems. For example, Emily postulated, “I don’t want
to spend 30 minutes of a class dealing with log-in issues.” Similarly, Olivia recounted an instance in which an AI
tool she was using froze up repeatedly during a speaking activity, disrupting the flow of the lesson. This left her
feeling discouraged about the possibility of integrating generative AI into her class.
Participants who were less experienced with AI generally reported higher anxiety. As Leo explained, “I don’t
know enough about [AI tools] to incorporate them into my teaching.” In contrast, those who were more familiar
with AI, such as Gabriel and Rob (who have spent time exploring ChatGPT), felt more confident leveraging the
technologies in the classroom.
In summary, developing TPACK and familiarity with AI tools plays a significant role in teachers’ technostress
levels. Some participants, including Gabriel and Rob, have identified strategies to alleviate technostress despite
the rapid progression of AI, inadequate training and technical difficulties. For example, self-driven TPACK
enhancement can be an effective strategy for managing technostress. Additional strategies are discussed in the
following section.
4.2. Theme 2: Factors alleviating technostress
4.2.1. Online engagement
Some participants have found solace in online forums and communities of practice that advance their TPACK,
ultimately helping them overcome the technostress associated with AI tools. These virtual spaces allow them to
share and learn from their fellow educators, fostering the confidence to use AI tools. For example, Jennifer
emphasized that helpful discussions on online forums alleviated her anxiety about incorporating AI into language
teaching, indicating an increase in her TPACK:
Joining online forums on AI tools on LinkedIn and reading about other teachers’ experiences has drastically
reduced my stress. Before, I felt so overwhelmed and alone, but now I realize we face similar challenges and
can support each other. It has given me the confidence to experiment with generative AI in my classroom.
(Jennifer)
Likewise, many participants started utilizing the Internet as a resource (e.g., reading blog posts or forum threads
about new AI tools) to increase their TPACK during the COVID-19 pandemic and have continued this practice.
This has promoted confidence and reduced technostress. Alice shared that she uses her commuting time to learn
about new AI tools and assess their applicability in her language classes. This proactive learning approach likely
helps strengthen her TPACK, thus facilitating the effective integration of AI in her classroom.
4.2.2. Gradual integration and realistic expectations
By setting moderate expectations and introducing AI tools into language learning activities incrementally, some
participants have become more comfortable with technology and gained confidence, reflecting growth in their
TPACK. Jennifer and Gabriel illustrated how focusing on mastering specific tasks and integrating one AI tool
into the classroom at a time has enhanced their TPACK and mitigated technostress:
I started by incorporating Memrise into my lessons and concentrated on learning its features and
functionalities. Once familiar with that tool, I gradually integrated other AI tools into my classes. This
approach helped me control my nervousness while building my confidence in using AI for language
instruction. (Jennifer)
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I use AI tools to grade short-answer questions or provide personalised feedback. I hope to use AI in my
teaching as I become more comfortable. (Gabriel)
Jennifer and Gabriel found that embracing a slow and steady approach to mastering new AI tools (and thus
developing their TPACK in phases) significantly reduced the stress associated with navigating the technological
landscape. Other participants stressed the importance of patience, as it takes time to adapt to new technologies.
For example, Henry said, “Integrating AI into my teaching isn’t an overnight transformation.” Likewise, Marge
stated, “I will take my time and learn at my own pace.” These findings suggest that creating a supportive
environment and encouraging teachers to adopt AI tools gradually can reduce their anxiety, increase overall
confidence and foster the development of TPACK.
Based on these findings, we suggest that gradual integration and setting realistic expectations can embody the
principle of fostering organic growth and adaptability among language teachers in the realm of generative AI.
The rapid pace of technological advancement can often be daunting and induce pressure to adopt these tools
immediately and seamlessly. However, as demonstrated by Jennifer and Gabriel, phasing in these technologies
gradually and focusing on depth (i.e., using AI to enhance learning) rather than breadth (i.e., how many tools
they are using) allows teachers to develop a holistic understanding of the technologies and an integration strategy
tailored to their unique teaching styles and classroom dynamics. This can also help teachers craft meaningful and
effective lesson plans that harness the strengths of AI tools, as they will not feel pressured to use all of their
features at once. Instead, they can experiment, iterate and refine their methods, resulting in a more authentic and
engaging learning experience for their students.
Moreover, the principle of adopting realistic expectations pertains not only to technical expertise but also to the
features of generative AI itself. Teachers should pinpoint where AI can genuinely enhance language teaching and
where traditional methods retain the upper hand, using their professional judgment to set boundaries. It is crucial
to recognize that, while AI is powerful, it does not render the human touch and expertise of language teachers
obsolete. In the age of generative AI, where change is the only constant, educators, institutions and policymakers
must collaborate to ensure that professional development initiatives foreground the importance of gradual
integration and realistic expectations. This approach not only reduces technostress but also optimizes the
teaching and learning process, ensuring that both educators and learners reap the full benefits of technological
advancements while preserving the essential human elements of education.
4.3. Theme 3: Strengthening the ecosystem with institutional support
4.3.1. Centralized resources and guidelines
The participants expressed the need for expanded university support systems to help them manage technostress,
develop TPACK and integrate AI tools effectively. This could include providing repositories of resources, best
practices and guidelines to help them enhance their students’ learning experiences. As Olivia mentioned, “A
centralized website with resources, guidelines and lesson plans would be invaluable in guiding me through the
process.” Similarly, Alice claimed, “A one-stop-shop for us to access reliable information and share experiences
would be awesome.”
4.3.2. Collaborative communities of practice
The teachers also claimed that it would be beneficial to have a supportive community with whom they could
collaborate, share experiences and co-construct TPACK. Charles explained how this could help:
Setting up opportunities for us to engage in a community of practice around AI tools would let us learn from
each other’s experiences and get ongoing peer support. (Charles)
A few teachers have taken the initiative to informally discuss the challenges with their colleagues, greatly
contributing to the development of their TPACK. Oliva and Rob agreed that such conversations provided a
valuable support network:
I also reached out to my colleagues for advice and support when I encountered difficulties with AI integration.
It’s reassuring to know that I’m not alone in this journey, and their encouragement has been invaluable.
(Olivia)
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We’d try different tools and techniques together and support each other … this has helped me ease my stress
about using AI in my teaching. (Rob)
However, the most effective communities of practice would also involve input from experts. Indeed, the
participants highlighted the need to communicate with educational technology and/or AI specialists who could
provide them with guidance and support on navigating the complexities of implementing new technologies.
Having experts to consult would greatly ease technostress, promote the development of TPACK and help
teachers use AI in their lessons more effectively. Martin commented:
I often felt lost when faced with technical issues or when trying to determine the best AI tool for specific
learning objectives. So, having an expert to consult would help me use AI in my lessons better. (Martin)
In a similar vein, Anna commented:
We need support to navigate the complexities of AI technologies to make informed decisions about how to
best use them in our classrooms. (Anna)
These findings suggest that allowing teachers to share their experiences and receive support from a healthy and
vibrant community of practice, as well as experts in the field, could help mitigate the technostress caused by the
emergence of AI.
4.3.3. Clear policies and incentives
Moreover, participants suggested that clear institutional policies and incentives to adopt AI could ease
technostress and foster the growth of TPACK. Emily explained, “It would be helpful if the university
administration and the faculty can provide clear policies around using AI tools.” Other participants noted that a
structured integration plan would ease their anxieties. Elizabeth described one way such a plan could work: “If
there was a clear, staged policy for phasing in AI over, say, the next three years, we wouldn’t feel as pressured to
change everything overnight.”
In addition, participants suggested that such measures could motivate teachers to develop their competence. For
example, Ethan explained:
I think it’d help if the administration developed clearer policies around AI tools, like giving incentives to
teachers who integrate these technologies into a certain number of lessons. Having that motivation would
make me more likely to take the time to learn about them. (Ethan)
In summary, the interview data underscores the importance of institutional support (e.g., centralized resources,
communities of practice, expert support, clear policies and incentives) in alleviating technostress among English
language teachers, fostering a healthy ecosystem for AI integration and supporting the development of TPACK.
At the time of the study, the support available at universities seems insufficient.
5. Discussion and conclusion
The present study has aimed to explore the factors contributing to technostress among English language teachers
since the emergence of generative AI tools and strategies to alleviate it, with an emphasis on TPACK. The
findings can be separated into three main categories: (1) factors that contribute to technostress; (2) factors that
alleviate technostress; and (3) strengthening institutional support.
The study has identified several factors that contribute to technostress among English language teachers who are
integrating GenAI tools into their teaching practices. They coalesced around several key dimensions of the
Technostress Creators Framework (Ragu-Nathan et al., 2008) and indicated that inadequate TPACK could lead
to technostress. One dimension, techno-complexity (i.e., continually acquiring new knowledge), manifested in
English language teachers’ struggle to keep up with the rapid pace of GenAI advancements. In this context,
techno-complexity concerns not only the technology itself but also the complexity of the pressure to develop
TPACK-related competencies. This is exacerbated by the fact that – in comparison to other technological
advancements – developments in generative AI have been incredibly fast and have the potential to radically
change the roles of teachers and higher education in general. With previous technological advancements, there
315
were transitional periods in which technologies were taken up gradually, first by early adopters and then
normalized within the profession (Bax, 2003). This allowed less technologically competent teachers time to
develop the skills and knowledge they needed (Meniado, 2023; Moorhouse, 2023). In this case, however,
technostress may be far-reaching and harder to mitigate in the short term.
The second dimension, techno-insecurity (i.e., apprehension regarding the potential applications of new
technology) manifested in concerns about job security and teachers’ skills becoming obsolete due to AI. This
insecurity was further heightened by an uncertainty about how best to integrate new GenAI tools into their
pedagogical practices, reflecting the third dimension of techno-uncertainty (e.g., Ragu-Nathan et al., 2008;
Tarafdar et al., 2011). Interestingly, our study found that TPACK played a critical role in how these factors
manifested. For instance, teachers with inadequate technological knowledge experienced more stress related to
techno-complexity, while those with insufficient pedagogical knowledge were more affected by techno-
insecurity. In addition, the teachers’ fears that software bugs would disrupt their lessons or reflect poorly on their
skills echo concerns about job security and being replaced by AI (Holmes & Tuomi, 2022; Ratten, 2020). This
fear contributes to technostress and underscores the need to address techno-insecurity in order to promote
teachers’ well-being. TPACK had a notable influence, suggesting a complex relationship with technostress that
warrants further investigation.
The second theme offered some insights into how technostress can be mitigated: targeted professional
development, online engagement, gradual integration of AI tools, and setting realistic expectations (e.g., Li &
Wang, 2021; Tarafdar et al., 2020). These strategies align with existing literature on mitigating techno-overload
(i.e., being forced to modify habits and work faster) and developing TPACK iteratively (Koehler et al., 2014).
However, our findings also indicate that – to fully harness the potential of GenAI in English language teaching –
teachers need to move beyond general TPACK and adopt GenAI-specific pedagogical models. This necessitates
more research into evidence-based practices for implementing GenAI with minimal technostress, which will
ensure that English language teachers can utilize it without feeling overwhelmed or insecure. To achieve this, it
will be crucial to strengthen institutional support (e.g., Chen et al., 2020; Jena, 2015).
Institutional support emerged as a critical factor in the analysis. It can take various forms, such as providing
resources, fostering communities of practice, offering expert guidance, and implementing clear policies on
GenAI integration. Such approaches enhance teachers’ technological knowledge and reduce techno-complexity
(Berger et al., 2023; Ertmer et al., 2012). Specifically, peer communities can provide emotional support and help
alleviate techno-insecurity.
In summary, this study answers the two research questions. With regard to RQ1, we have identified several
factors contributing to technostress among English language teachers in Hong Kong using generative AI. They
include techno-complexity, techno-insecurity, and techno-uncertainty. These interconnected factors highlight the
challenges that educators face in the rapidly evolving landscape of AI technology. Regarding RQ2, this study has
uncovered strategies that can effectively alleviate technostress, including targeted professional development,
online engagement, and gradual integration of AI tools. The importance of bolstering TPACK and the crucial
role of institutional support was also emphasized. These findings offer valuable insights into the complexities of
integrating AI in English language teaching and underscore the importance of addressing technostress to ensure
effective and sustainable AI integration. Future research should explore these dynamics further and develop
evidence-based practices for using AI effectively in the classroom.
5.1. Limitations
While this exploratory interpretive study provides initial insight, it also has some limitations, including the
limited number of participants, which may limit the generalizability of the findings. However, a sample of 16
teachers can be deemed sufficient to generate a “new and richly textured understanding” (Sandelowsky, 1996, p.
183) of the phenomenon under investigation. In the interpretivist paradigm, the emphasis is placed on relevance
rather than rigour at an axiological level. Consequently, each reader should individually evaluate the relevance of
the findings to their own professional or personal context and specific circumstances (Merriam & Tisdell, 2016).
Future research should use larger, more diverse samples to enhance the credibility of the findings. This study
also relied solely on self-reported data, which can be subject to bias and retrospective memory errors. Later
research could utilize questionnaires to assess technostress systematically, conduct classroom observations to
obtain objective evidence of teacher–AI interactions and incorporate interviews with other stakeholders to gain
insight into institutional support and educational policies.
316
5.2. Practical implications
Technostress is a known and concerning effect of technological advancements, which must be considered when
teachers are asked to introduce new technologies into their professional practices. However, in the past, the pace
of integration was slower, allowing language teachers to develop the relevant TPACK. We argue that generative
AI is different: this new technology is particularly disruptive and requires timely support from higher education
institutions and management. This support should be comprehensive and sustained, prioritizing both teacher and
student well-being.
As universities adopt a more AI-positive stance, they need to appreciate the transitional disruption teachers face
and offer a supportive, non-judgmental environment. Encouraging instructors to experiment and take risks with
new strategies is crucial in reducing technostress. To address this in practice, we propose that higher education
institutions take the following steps:
• Implement comprehensive training sessions for English language teachers. These sessions should include an
introductory course on AI fundamentals, providing teachers with a foundational understanding of how AI
algorithms work. Further training should involve hands-on workshops where teachers can explore the
advanced features of specific AI tools for language teaching. This will ensure that teachers comprehend the
full range of tools available to them and adapt their teaching strategies accordingly.
• Form professional learning communities. These communities should be organized in terms of monthly
meetups, either in-person or virtual, that provide teachers with a platform to share their experiences,
challenges, and successes. An online forum or shared Google Drive could be used for the continuous
exchange of ideas and resources, fostering a sense of collaboration and shared learning.
• Set up regular feedback sessions. These sessions should involve both teachers and AI experts from the
institution and encourage open dialogue. Feedback could be collected through online surveys or one-on-one
interviews and then analyzed to identify common challenges and successful strategies. This feedback can be
used to continuously refine training modules and support systems.
• Equip teachers with resources and training on ethical issues. Topics could include data privacy, algorithmic
bias, and the impact of AI on student learning outcomes. This will ensure teachers make informed, ethical
decisions in their classrooms.
• Provide customizable AI toolkits. These toolkits should include varying levels of AI assistance, different
language options, and adaptable learning paths based on student proficiency levels. This flexibility allows
teachers to align the AI tools with their teaching styles, classroom needs, and student demographics.
Incorporating these practical measures will enable teachers to not just survive but thrive in the evolving
technological landscape. By investing in robust support structures and recognizing the challenges, higher
education institutions can pave the way for effective and meaningful AI integration in English language
classrooms.
5.3. Theoretical implications
While this study provides some actionable recommendations for higher education institutions, its findings also
contribute to the theoretical understanding of technostress in the context of English language teaching and
generative AI tools. It underscores the role TPACK plays in teachers’ ability to handle technostress and expands
the current body of knowledge by exploring how technostress manifests. The finding that rapid AI advancement
contributes to techno-overload and techno-uncertainty aligns with crucial elements of the Technostress Creators
Framework (Ragu-Nahtan et al., 2008) and provides empirical support for the concept of technology–role stress
(Ayyagari et al., 2011). This study also highlights the need for further investigation into targeted training
interventions, TPACK enhancement and self-efficacy, which can inform the development of optimized
programmes and evidence-based policies that promote teacher well-being and the productive use of technology.
5.4. Conclusion
In conclusion, this study has provided valuable insights into the factors that contribute to and alleviate
technostress among English language teachers using generative AI tools, highlighting the role of TPACK.
Technostress among English language teachers is a multifaceted issue. By exploring these factors and
emphasizing the importance of institutional support and TPACK, we have contributed to the theoretical
317
understanding and practical application of these concepts. This research provides a foundation for future studies
examining teacher well-being, effective AI integration and the role of TPACK in English language teaching.
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