Mohamad Almashour
Hesham Abdel Karim Aldamen
Marwan Jarrah- Department of English Language and Literature, School of Foreign Languages, The University of Jordan, Amman, Jordan
Introduction: The integration of artificial intelligence (AI) in education is reshaping classroom practices and teacher identity, particularly in English as a Foreign Language (EFL) contexts. This study investigates undergraduate perceptions of how AI impacts EFL teacher roles in Jordanian classrooms.
Methods: Using a qualitative design, semi-structured interviews were conducted with 20 third- and fourth-year EFL students at the University of Jordan. Thematic analysis was applied to identify patterns in student responses.
Results: Four major themes emerged: (1) the diminished centrality of teachers, (2) AI as a pedagogical double-edged sword, (3) the irreplaceable human element, and (4) reframing authority and expertise.
Discussion: While students appreciate AI’s efficiency, they emphasize the enduring value of teacher empathy, cultural understanding, and relational engagement. The findings highlight the need for balanced AI integration that preserves human dimensions of teaching.
Conclusion: The study underscores the evolving nature of teacher identity in AI-mediated contexts and provides implications for pedagogical training and policy in Jordan and similar contexts.
1 Introduction
The rapid advancement of Artificial Intelligence (AI) technologies has triggered a paradigm shift in global education systems, with applications ranging from adaptive learning and automated assessment to intelligent tutoring systems and natural language processing tools. These innovations are transforming not only how students learn, but also how educators teach and define their professional roles. In English as a Foreign Language (EFL) contexts in particular, AI-supported platforms, such as chatbots, grammar checkers, and language-learning algorithms, are becoming integral to classroom instruction, redefining pedagogical practices and the very nature of teacher-student interaction (Zawacki-Richter et al., 2019).
While AI is often celebrated for its potential to enhance personalization, efficiency, and learning outcomes, its deeper implications for teacher identity remain underexplored, particularly in non-Western and multilingual educational settings (Kim, 2023; Tian and Wang, 2025; Yu et al., 2025; Bittle and El-Gayar, 2025). Although a number of regional studies have examined AI’s pedagogical integration in higher education (e.g., Alotaibi and Alshehri, 2023), few address the identity-related transformations EFL teachers experience, particularly as viewed through students’ eyes. Thus, this study responds to a growing but still underdeveloped body of literature by offering a student-centered perspective on how AI reshapes teacher identity in Jordan’s EFL classrooms, where sociocultural values, affective ties, and digital innovation intersect in complex ways.
Teacher identity is a dynamic, multifaceted construct shaped by personal beliefs, institutional discourses, sociocultural norms, and classroom experiences (Beauchamp and Thomas, 2009; Trent, 2016). It is continuously negotiated in response to contextual pressures and emerging educational paradigms, including those driven by digital innovation. The introduction of AI into EFL classrooms presents both opportunities and tensions: on one hand, it allows teachers to focus on creative and relational tasks; on the other, it may challenge their professional authority, autonomy, and emotional labor.
The emotional labor involved in EFL teaching—such as expressing empathy, building rapport, and responding to students’ affective needs—has traditionally been central to teacher identity (Benesch, 2012; Zembylas, 2005). In AI-mediated classrooms, however, students may perceive a reduction in such emotional dimensions, especially when AI tools replace teacher feedback or interaction. These emotional dynamics are particularly salient in collectivist and high-context cultures like Jordan, where relational warmth and immediacy are often markers of pedagogical credibility. These tensions are further exacerbated in regions where teacher-centered pedagogies remain prevalent and where technological infrastructure and policy support are still developing.
In the Arab world, and Jordan in particular, the integration of AI in education is gaining momentum in response to national strategies for digital transformation and educational reform. However, empirical research on how AI is reshaping the roles and identities of EFL teachers in Jordanian universities remains scarce. Existing studies have primarily focused on teachers’ attitudes toward AI or on its impact on language learning outcomes, leaving a critical gap in understanding how students interpret the evolving teacher role in AI-mediated environments.
Despite growing global interest in AI’s influence on teaching and learning, there is a significant lack of studies that explore how students, especially those in multilingual, postcolonial, and culturally rooted educational systems, perceive the identity shifts of their teachers in response to AI integration. In the Jordanian context, no known study has investigated the perspectives of undergraduate EFL students regarding how AI technologies are influencing their instructors’ authority, instructional roles, or relational presence. Given that students are co-constructors of teacher identity through their interactions, expectations, and feedback, this gap demands urgent scholarly attention.
This study seeks to address this gap by investigating the influence of AI integration on teacher identity in EFL education from the perspective of undergraduate students at the University of Jordan. Specifically, it focuses on third- and fourth-year students who have had sustained exposure to EFL instruction and are better positioned to reflect on shifts in teacher roles and authority over time.
The study is guided by the following research questions:
i. How do undergraduate EFL students perceive the impact of AI technologies on the professional roles and identity of their language teachers?
ii. In what ways do AI applications in EFL classrooms influence students’ perceptions of teacher authority, relational connection, and instructional responsibilities?
iii. Which aspects of teacher identity do students believe are most affected, or remain irreplaceable, when AI is integrated into EFL instruction?
To situate this inquiry within the sociocultural realities of Jordanian EFL education, this study adopts the lens of culturally relevant pedagogy (CRP), as developed by Ladson-Billings (1995, 2014). CRP emphasizes the importance of sustaining students’ cultural integrity while promoting academic success and critical consciousness. In the context of Jordan, where students’ linguistic, religious, and social values are deeply intertwined with their educational experiences, CRP offers a more appropriate framework than “cultural responsiveness.” While the latter typically focuses on teacher sensitivity to cultural differences, CRP asserts that pedagogy must actively draw from and affirm students’ cultural backgrounds to foster meaningful learning. In AI-mediated classrooms, where global technologies often reflect Western norms, CRP encourages instructors to maintain pedagogical practices that resonate with local epistemologies and relational expectations. Thus, this study foregrounds culturally relevant pedagogy as a theoretical anchor for interpreting how students in Jordan perceive the evolving role and identity of their EFL teachers in the face of technological transformation.
2 Literature review
2.1 Artificial intelligence in higher education and EFL contexts
Artificial Intelligence (AI) is playing an increasingly prominent role in English as a Foreign Language (EFL) instruction, offering diverse benefits that extend across linguistic, cognitive, and affective domains. AI-enhanced tools such as grammar correction platforms, intelligent tutoring systems, and automated writing assistants have been shown to support real-time feedback, foster learner autonomy, and improve language proficiency, particularly in writing and grammar (Zawacki-Richter et al., 2019). These tools allow students to receive immediate, individualized guidance, promoting more efficient and self-regulated learning experiences. Additionally, conversational AI, including chatbots, can create low-pressure environments for oral practice, helping learners build fluency while reducing speaking anxiety (Wiboolyasarin et al., 2025).
As AI technologies become more embedded in language instruction, they are reshaping classroom dynamics and instructional authority. Teachers are increasingly required to shift from knowledge providers to facilitators who manage, interpret, or even compete with AI-generated content (Gentile et al., 2023). In EFL settings, where language norms, authorship, and authority are already under negotiation, this shift can provoke new tensions in how teachers perceive and perform their professional identities (Zaman et al., 2024). AI’s real-time feedback and automated responses, while enhancing learner autonomy can also marginalize teachers’ traditional roles and call into question their relevance in an AI-mediated classroom (Namaziandost and Rezai, 2024; Han et al., 2025). However, the pedagogical value of AI is not universally positive and depends significantly on how these tools are implemented. As Holmes et al. (2022) argue, the success of AI-mediated learning is contingent on its integration within human-centered teaching frameworks. Derakhshan (2025) found that while generative AI improved task efficiency and language output, students’ motivation and emotional engagement were primarily driven by teacher presence and relational interaction. Similarly, Mahapatra (2024) noted that although AI-generated feedback enhanced ESL students’ academic writing, learners expressed diminished engagement in the absence of dialogic teacher feedback.
These findings suggest that AI’s contribution to EFL education is most impactful when it augments rather than replaces the teacher’s role. The relational, emotional, and cultural dimensions of teaching remain central to student development and cannot be fully replicated by AI systems, regardless of their technical sophistication.
2.2 Understanding teacher identity: a dynamic and contextual construct
This study adopts a sociocultural perspective on teacher identity, emphasizing that identity is not a fixed trait but is co-constructed through social interaction, institutional positioning, relational engagement, and cultural discourse (Holland et al., 1998; Vygotsky, 1978; Norton, 2013). Sociocultural identity theory posits that individuals form their sense of self through participation in culturally and historically situated practices, mediated by tools—including language and technology. In EFL classrooms, teacher identity is shaped by how instructors navigate expectations, respond to student feedback, and integrate emerging technologies such as AI. This theoretical lens helps illuminate the shifting dynamics of authority, care, and expertise as AI becomes more embedded in instructional routines.
Teacher identity refers to how educators perceive and perform their roles within specific sociocultural, institutional, and relational contexts. It is a dynamic, multifaceted construct shaped by beliefs, experiences, interactions with students, and institutional expectations (Beauchamp and Thomas, 2009; Trent, 2016). In this study, identity is understood as being continually negotiated in response to technological change and co-constructed through student perceptions.
Recent scholarship further underscores that identity is mediated by technological affordances. El-Soussi (2025), for example, introduces the Teacher Identity Continuum (TIC), categorizing educators as Digital Adapters, Ambivalents, or Resisters based on their engagement with digital tools. This framework complements sociocultural models by emphasizing that identity shifts are non-linear, emotionally invested, and shaped by tensions and negotiations dynamics echoed in student narratives from this study.
In the context of language teaching, identity work is especially complex due to the need for continual negotiation between globalized language norms and local sociocultural realities (Yazan, 2018). Language teachers often serve not only as content deliverers but also as cultural brokers, emotional supporters, and agents of socialization. These relational roles require high levels of interpersonal engagement and affective awareness dimensions that AI technologies do not yet adequately replicate.
2.3 AI and student perceptions of teacher identity
The integration of AI into educational contexts has not only transformed pedagogical practices but also redefined how students perceive their teachers’ professional identities. In EFL settings, where language instruction has increasingly embraced student-centered approaches, AI contributes to an ongoing shift in the teacher’s role—from that of an authoritative knowledge-holder to a facilitator or co-navigator of learning (Zaman et al., 2024). Rather than being the sole driver of change, AI acts in concert with broader pedagogical movements that prioritize learner agency, dialogic instruction, and constructivist engagement. This redefinition affects how students evaluate teacher competence, authority, and presence. Rather than passive recipients of instruction, students become active agents in shaping teacher identity through their responses to and expectations of AI-mediated instruction. This aligns with sociocultural identity theory, which views identity as a dynamic, co-constructed phenomenon emerging from social interaction and cultural context (Yazan, 2018).
Moreover, Pishghadam et al.’s (2022) identity framework, particularly the dimensions of emotioncy, capital, and mirrors of power, offers a valuable lens for understanding how students’ emotional engagement and perceived relational proximity influence their construction of teacher identity in AI-enhanced classrooms. Empirical work by Ghiasvand and Seyri (2025) further demonstrates that the presence of AI alters not just instructional methods but the way teachers and learners mutually shape one another’s professional and pedagogical roles. Therefore, students’ perceptions are not merely observational; they play a formative role in the evolving landscape of teacher identity in technology-rich learning environments.
2.4 AI and the reconfiguration of teaching roles
AI’s growing presence in classrooms has initiated a redefinition of teaching roles. As Cope et al. (2020) suggest that teachers may move away from being information transmitters toward becoming designers of learning experiences or facilitators of AI-human collaboration. In this new paradigm, AI may assume repetitive or administrative tasks (e.g., grading, feedback generation), thereby freeing educators to focus on creativity, empathy, and critical thinking instruction (Kim, 2023; Shah, 2023; Takona, 2024).
Building on this reconfiguration of teaching roles, Kim (2023) introduces a staged model of Teacher–AI Collaboration (TAC), progressing from teachers as passive recipients of AI tools to active users and ultimately as collaborative partners with AI systems. This framework highlights that effective integration requires more than technical competence, it demands critical digital pedagogy, reflexivity, and institutional support. The model supports this study’s finding that students value teachers who embrace AI strategically, using it to augment, not replace, their pedagogical authority and interpersonal engagement. However, this reconfiguration is not without tension. Teachers may experience techno-stress, loss of professional agency, or ethical dilemmas surrounding surveillance and algorithmic bias. These shifts may also evoke existential concerns about teacher relevance, autonomy, and emotional connection, particularly in contexts where humanistic teaching is deeply valued. Mollick and Mollick (2023) have proposed a framework of seven AI roles in classrooms, including tutor, mentor, and teammate, that frames AI as a pedagogical actor rather than a tool. This typology reflects emerging views that redefine teacher identity in AI-integrated settings, suggesting that educators are not being replaced but repositioned.
Emerging conceptual models help frame these shifts. Pishghadam et al. (2022) introduced a six-component identity framework, including mirrors of power, discourse, imagination, investment, emotioncy, and capital, that emphasizes how institutional and technological forces shape identity development. This model is highly relevant in AI-mediated classrooms, where the interplay between human and machine authority influences how teachers see themselves and are seen by others. Complementing these models, Tian and Wang (2025) emphasize that AI integration in language classrooms must go beyond efficiency to consider translanguaging and teacher emotional labor. In this study, we extend this discussion by incorporating culturally relevant pedagogy (Ladson-Billings, 1995, 2014), which emphasizes affirming students’ cultural identities and connecting instruction to local lived realities in EFL contexts like Jordan. Their interdisciplinary collection highlights how AI, if not critically mediated, can suppress the linguistic and cultural plurality essential to EFL instruction. Contributors such as Back and Kabulis argue for maintaining teacher-authored content to preserve relational depth, while Sawin’s ecological approach suggests reimagining teacher preparation to include AI ethics, multilingual awareness, and emotional intelligence. These insights reinforce the centrality of the human element, particularly in culturally rooted contexts like Jordan.
Recent research by Kabadayı (2024), using the Technology Acceptance Model (TAM), examined how AI adoption affects teacher identity among 52 higher education instructors in Turkey. The study found that increased familiarity with AI and perceptions of its usefulness significantly enhance teachers’ sense of professional identity, while ease of use had no significant impact. Notably, younger educators reported more positive attitudes toward AI integration, suggesting that age may mediate digital adaptability and identity shifts. These findings highlight the role of technological confidence and pedagogical utility in shaping teachers’ evolving roles and support a growing consensus that AI reshapes rather than replaces professional identity.
2.5 Student perspectives: a missing link in identity research
Although a growing number of studies address AI’s impact on teachers, most do so from the perspective of educators, policymakers, or technologists. Far fewer explore students’ perceptions of how AI integration affects their teachers’ identity, a striking omission given that students are co-constructors of classroom realities and active agents in shaping their teachers’ professional image (Beijaard et al., 2004; Trent, 2016).
This gap is particularly relevant in EFL classrooms, where students’ perceptions of teacher presence, linguistic authority, and relational approach play a significant role in their motivation and engagement. When AI systems begin to take over tasks such as explanation, assessment, or even emotional support, students may recalibrate their expectations of what it means to be a teacher. Exploring these shifts can provide valuable insight into the sociocognitive impacts of AI and contribute to more equitable and human-centered technology integration.
Ghiasvand and Seyri (2025) conducted a qualitative study exploring how artificial intelligence (AI) shapes EFL teachers’ professional identities in Iran. Drawing on a post-structuralist framework and using collaborative reflection panels and narrative frames, the study identified six key areas where AI influenced identity reconstruction: transformation of teacher roles, pedagogical alignment, personalization and collaboration in instruction, enhancement of expertise, promotion of reflective practice, and technological literacy. Their findings underscore the fluidity of teacher identity in AI-enhanced educational contexts, arguing that AI acts not merely as a tool but as an agent of identity (re)construction. This research fills a notable gap by examining AI’s impact on identity, rather than solely on pedagogy or learning outcomes, and offers theoretical and practical implications for teacher training and L2 educational policy.
2.6 The Arab and Jordanian context: underexplored but vital
In the Arab world, efforts to digitize education are accelerating in response to global competitiveness and national modernization agendas. In Jordan, the Ministry of Education has actively promoted the use of educational technology across all levels of schooling, with universities like the University of Jordan adopting AI-enhanced platforms for learning management and assessment.
Despite this momentum, empirical research on the cultural, emotional, and relational dimensions of AI integration remains limited. A recent study by Okolo et al. (2024) emphasizes that while AI systems can personalize language education and increase efficiency, they cannot replace the human teacher’s role in fostering empathy, sociocultural awareness, and emotional connection. Their findings highlight the importance of contextual understanding and individualized teacher-student rapport—particularly in language education. These insights strongly align with the Jordanian context, where EFL instruction is rooted in culturally grounded, affective, and relational teaching practices.
In the Arab world, digitization efforts are accelerating, and while foundational studies, such as Alotaibi and Alshehri’s (2023) review of AI in Saudi higher education, document institutional enthusiasm and infrastructural challenges, identity-focused analyses remain sparse. Their findings, alongside recent critiques of AI’s cultural and epistemic implications (Tian and Wang, 2025), suggest a need for more critically grounded inquiries into how AI impacts not only learning outcomes but also affective and relational dynamics in postcolonial, multilingual contexts.
Drawing on these insights, this study avoids simplistic claims of research scarcity and instead acknowledges an emergent body of literature while foregrounding the lack of student-centered, identity-oriented investigations in Jordan. While Jordan and Saudi Arabia differ in sociopolitical structure and educational governance, both countries are pursuing aggressive AI integration agendas as part of broader national transformation plans. Comparative regional studies (e.g., Alotaibi and Alshehri, 2023) can offer insight into shared infrastructural and pedagogical challenges relevant to the Jordanian context.
While the pedagogical affordances of AI tools have been widely documented, the ethical dimensions of their use remain under-theorized in EFL contexts. Issues such as data privacy, algorithmic bias, over-reliance on automated feedback, and the erosion of teacher agency raise concerns about equity, transparency, and student trust (Selwyn, 2019; Holmes et al., 2022). In the Jordanian context, where institutional guidelines on AI use are still evolving, instructors must navigate these uncertainties without clear ethical frameworks. Students’ perceptions of teacher identity may be shaped not only by pedagogical strategies but also by how teachers manage these ethical ambiguities—whether they critically reflect on tool limitations, explain their use of AI, or invite students to engage in ethical dialogue.
To date, no known study has examined how Jordanian undergraduate students, particularly those in the advanced stages of their degree, perceive the ways in which AI influences their teachers’ identity, presence, and pedagogical role. This study fills that gap by focusing on third- and fourth-year EFL students at the University of Jordan—learners who are likely to have accumulated enough classroom exposure to reflect critically on these changes.
2.7 Conceptual framework and theoretical anchors
This study is guided by two complementary theoretical lenses:
i. Sociocultural Identity Theory, which emphasizes the socially negotiated nature of identity and the contextual factors (e.g., discourse, culture, policy) that influence its formation (Beauchamp and Thomas, 2009; Yazan, 2018); and
ii. Pishghadam et al.’s (2022) Identity Framework, which foregrounds emotional, cultural, and institutional dimensions, especially relevant in AI-mediated environments.
In addition to sociocultural identity theory and Pishghadam et al.’s (2022) identity framework, this study incorporates postcolonial critiques of AI that expose the algorithmic reproduction of dominant epistemologies, the marginalization of local pedagogical values, and the cultural asymmetries embedded in global educational technologies (Back & Kabulis, in Tian and Wang, 2025). These perspectives are particularly salient in the Jordanian context, where AI integration often reflects broader geopolitical and linguistic hierarchies.
In this context, the term postcolonialism refers to the lingering cultural, linguistic, and epistemic asymmetries that shape how global technologies, such as AI, are implemented in Jordanian classrooms. These technologies often reflect dominant Western pedagogical norms, which may conflict with local values, language ideologies, and teacher-student relational expectations. Additionally, the study draws on culturally relevant pedagogy to interpret how local cultural knowledge and student-teacher relationships shape identity negotiations in AI-mediated instruction.
AI is therefore not treated as a neutral pedagogical tool. As recent scholarship emphasizes, algorithmic systems are shaped by corporate interests, data-driven logics, and sociotechnical infrastructures that may reinforce structural inequities (Tian and Wang, 2025; Cope et al., 2020). Such dynamics demand closer scrutiny in educational research, especially in postcolonial, multilingual settings where imported technologies may conflict with local values and identity norms.
Taken together, these theoretical lenses clarify how students interpret the evolving role of teachers in AI-mediated EFL classrooms and how these perceptions intersect with broader identity reconfigurations. They also point to the need for future research that foregrounds the political economy of educational AI and its implications for justice, legitimacy, and epistemic inclusion.
3 Methodology
3.1 Research design
This study adopts a qualitative research design to explore undergraduate EFL students’ perceptions of how artificial intelligence (AI) integration influences their teachers’ professional identity. Qualitative inquiry is well-suited for investigating socially constructed phenomena such as identity, as it allows for an in-depth understanding of participants’ beliefs, interpretations, and contextual experiences (Merriam and Tisdell, 2016; Creswell and Poth, 2018). By centering student voices, this research aims to uncover nuanced insights into how learners interpret shifts in the roles, authority, and relational presence of their EFL instructors in AI-mediated learning environments.
3.2 Research context and participants
The study was conducted at the University of Jordan, one of the leading higher education institutions in the Arab region. AI integration into English language instruction at the university has recently begun through institutional initiatives. While students interacted with multiple EFL instructors, the instructors’ familiarity and comfort with AI tools varied significantly. Importantly, the university offered optional workshops focused on two distinct strands of AI-related training: AI technical training and AI pedagogical training. The former concentrated on operational skills, such as using platforms like Grammarly, QuillBot, and ChatGPT, whereas the latter focused on incorporating AI tools meaningfully into formative feedback, assessment design, and classroom discourse. Some instructors had attended technical training only, leading to a tool-centered approach to AI use, while others who engaged with the pedagogical workshops demonstrated more strategic integration of AI aligned with instructional goals. This divide was reflected in the students’ perceptions, with some describing their teachers as merely allowing AI use passively, and others noting more guided, dialogic, or even critical engagement with AI outputs in the classroom. Including this contextual information is essential for interpreting the heterogeneity in student narratives across cases.
The participants were drawn from third- and fourth-year undergraduate students enrolled in the university’s English Language and Literature program. These students were purposively selected based on two criteria: (1) their extended exposure to EFL instruction, and (2) their familiarity with AI-supported educational tools (e.g., grammar checkers, learning management systems with AI functions, or AI-assisted writing tools such as Grammarly or ChatGPT).
A total of 20 participants (10 males and 10 females) were selected using purposive sampling to ensure maximum variation in academic achievement, digital literacy, and AI usage experience. This sample size is consistent with recommendations for achieving thematic saturation in qualitative studies (Guest et al., 2006).
While the sample size (n = 20) may appear limited, it aligns with established qualitative research standards for achieving thematic saturation in focused, interpretive studies (Guest et al., 2006). The aim of this research is not statistical generalizability but analytic transferability through rich, thick description and contextual specificity. The single-site focus allows for depth of inquiry, capturing the nuanced perspectives of students within a well-defined institutional and cultural setting.
3.3 Data collection
Data were collected through semi-structured interviews, each lasting between 30 and 45 min. Participants were selected using purposive nonrandom sampling to ensure rich insights into teacher identity and AI integration in EFL classrooms. An interview protocol was developed based on themes emerging from the literature, particularly teacher identity frameworks (Beauchamp and Thomas, 2009; Pishghadam et al., 2022), and pilot-tested with two students for clarity and flow.
Sample interview questions included:
i. “How would you describe your EFL teachers before and after AI tools were introduced into the classroom?”
ii. “Do you feel your teachers rely more or less on technology now, and how does that affect your relationship with them?”
iii. “What do you think are the most important qualities a language teacher should have today?”
Interviews were conducted in English and Arabic, depending on the participants’ preference, and were audio-recorded with informed consent.
3.4 Data analysis
The audio-recorded interviews were transcribed verbatim and analyzed using thematic analysis (Braun and Clarke, 2006). The analysis followed six recursive steps: familiarization with the data, initial coding, searching for themes, reviewing themes, defining and naming themes, and producing the report.
Coding was both deductive (informed by identity theory and prior literature) and inductive (open to emergent student meanings). The analysis was facilitated using NVivo 12 software to enhance transparency and traceability of theme development.
Key identity constructs, such as authority, presence, role negotiation, and human-AI comparison, guided the development of thematic categories. Triangulation was achieved by comparing coding results across multiple transcripts and through peer debriefing with a qualitative research expert at the University of Jordan.
To enhance analytic rigor, an initial subset of transcripts (n = 4) was independently coded by a second researcher familiar with qualitative inquiry in language education. A consensus-based approach to inter-coder reliability was adopted, whereby both coders independently reviewed an initial subset of transcripts and met to discuss divergent interpretations. Disagreements were resolved through reflective dialogue grounded in the theoretical framework, and coding categories were iteratively refined. This approach enhanced interpretive validity while maintaining theoretical alignment and reflexivity throughout the analysis.
The remaining transcripts were coded by the primary researcher, using the refined codebook within NVivo 12. An audit trail was maintained to document coding decisions, theme evolution, and researcher reflections throughout the process. This approach, combined with peer debriefing and thick description, contributed to the overall credibility and dependability of the analysis.
3.5 Trustworthiness
To ensure the credibility and trustworthiness of the study, several strategies were employed:
i. Member checking: Participants were invited to review their interview transcripts for accuracy and confirm the researcher’s interpretations of key quotes.
ii. Thick description: Contextual details about the university, course types, and classroom dynamics were provided to support transferability.
iii. Audit trail: A research log documenting methodological decision, coding evolution, and reflective notes was maintained.
iv. Peer review: An external reviewer with expertise in language education and qualitative methods reviewed the coding framework and final themes.
Despite efforts to ensure trustworthiness, this study has certain limitations. First, the study was conducted at a single institution, limiting generalizability across institutional and national contexts. Second, it includes only student perspectives, which, while valuable, provide a partial view of identity construction. The absence of teacher voices constrains the analysis to one side of the relational dynamic central to identity co-construction. Future studies should incorporate teacher narratives to provide a more holistic understanding of how professional identity is experienced and negotiated. Second, although interviews were semi-structured and reflexive, steps were taken to mitigate potential interviewer influence on participant responses. The interviewer adopted a non-evaluative stance, used open-ended prompts, and emphasized that there were no “correct” answers. Efforts were also made to minimize power imbalances by assuring participants of confidentiality and encouraging them to speak in their preferred language. Additionally, reflexive journaling was used to monitor positionality and reduce bias during data collection. While the possibility of subtle influence remains a concern in any qualitative research, these measures helped protect the validity of the findings and foster authentic student expression. Third, while participants were invited to verify their transcripts and initial interpretations, the full thematic framework was later shared with a subset of participants (n = 5) in follow-up sessions. Their feedback was incorporated to confirm thematic resonance and address interpretive ambiguities. This partial member-checking strategy, though not exhaustive, helped ensure that the emergent findings were grounded in participant meaning-making and sociocultural realities.
3.6 Ethical considerations
This study received ethical clearance from the Institutional Review Board (IRB) at the University of Jordan. All participants signed informed consent forms, were assured of anonymity, and were informed of their right to withdraw from the study at any time without penalty. Data were anonymized and securely stored in encrypted formats accessible only to the research team.
4 Findings and discussion
This section presents the findings from the semi-structured interviews with 20 third- and fourth-year EFL students at the University of Jordan. Through thematic analysis, four interrelated themes emerged that illustrate students’ perceptions of the evolving role and identity of their teachers in AI-mediated classrooms. While selected quotes are used for illustrative clarity, these findings are grounded in rich contributions from all 20 participants, many of whom spoke across multiple themes. These themes are discussed individually and situated within relevant theoretical and empirical frameworks. Table 1 below provides a summary of selected participant statements, categorized by theme, to offer a broader view of the variety of responses underpinning the findings.
Table 1. Sample student responses by theme.
These selected statements reflect the range of student perceptions across the identified themes and serve as a foundation for the thematic analysis presented in the following sections. Each theme is explored in depth to highlight not only the commonalities in student responses, but also the tensions, nuances, and implications that emerge as AI reshapes teacher identity in EFL classrooms. The discussion draws on these responses in conjunction with relevant theoretical frameworks to offer a comprehensive understanding of the evolving student-teacher dynamic in AI-enhanced learning environments.
4.1 The diminished centrality of the teacher
Participants commonly reported a shift in how they seek academic support, describing how AI tools such as ChatGPT and Grammarly have become their primary sources for clarification, correction, and guidance. Many students no longer perceive the teacher as their first or most authoritative point of reference, particularly for technical language tasks. Instead, they turn to AI systems that offer immediate, judgment-free, and always-available assistance. These preferences highlight a growing reliance on digital platforms for linguistic autonomy and raise questions about the evolving status of teachers in AI-integrated educational spaces.
One student remarked, “Before, I used to go to my teacher for grammar corrections or writing help. Now I just use Grammarly or ChatGPT. They give me answers instantly. It feels like I do not want to ‘bother’ the teacher anymore” (Participant 7, Female, 4th year).
Another echoed this preference for autonomy, stating, “We had to write essays last semester, and honestly, most of us just wrote them with AI help first, then submitted them directly. The teacher checked them, but we did not really need her help like before” (Participant 2, Male, 3rd year).
Several participants described AI as a less intimidating and more efficient alternative to asking questions in class: “ChatGPT is like a tutor that does not get tired or annoyed. Teachers have limits, they get frustrated or busy. ChatGPT is always there, and fast” (Participant 10, Female, 4th year).
Another student reflected on the social comfort AI provides: “AI tools give you what you ask, without judgment. Some students feel shy to ask teachers too many questions, so AI is safer. Less pressure” (Participant 14, Male, 4th year).
Another student mentioned: “Sometimes I feel like the AI already knows everything I need. The teacher just confirms it, like a second opinion, not the main one” (Participant 16, Female, 3rd year).
Another one mentioned “It’s faster to use AI than wait for office hours. We’re used to answers now, not delays” (Participant 5, Male, 4th year).
Taken together, these comments reflect a significant reconfiguration of classroom hierarchies and power dynamics. Whereas teachers were once viewed as the exclusive gatekeepers of language knowledge, students now turn to AI for rapid, reliable, and stigma-free support. This digital redirection of authority suggests not only a pragmatic shift but also a deeper epistemological transformation, one that positions AI as a parallel, if not preferred, educator in certain domains.
The implications of this shift are multifaceted. On a relational level, the move toward AI may inadvertently weaken the trust-based connection that typically characterizes effective language instruction. Teachers risk becoming peripheral figures, especially when their instructional value is seen as secondary to the algorithmic precision of AI. On a cognitive level, this shift indicates a redirection of “emotioncy,” or emotional and sensory investment, from the teacher to the machine (Pishghadam et al., 2022). While this may appear to undermine traditional interpersonal feedback, dialogic learning in AI-mediated classrooms can be re-conceptualized as a triadic interaction involving the student, the teacher, and the AI system. In this model, students not only engage in meaning-making with their instructors but also critically negotiate and reflect on AI-generated input—an interaction that is dialogic in nature when scaffolded by the teacher. Rather than displacing teacher-student dialogue, well-integrated AI tools can become part of an expanded dialogic ecology that includes human and algorithmic interlocutors.
Additionally, this trend may challenge teachers’ sense of professional identity and self-efficacy. When students bypass teacher input in favor of AI solutions, instructors may perceive their roles as devalued or redundant. This experience can erode confidence and motivation, particularly in EFL settings where feedback, modeling, and emotional encouragement are core pedagogical strategies.
Ultimately, the diminished centrality of the teacher in AI-supported classrooms reflects a broader educational paradigm shift, one that necessitates critical reflection on the boundaries between human and machine instruction, and on how to sustain meaningful teacher-student relationships in a technology-saturated academic environment. While students expressed a growing reliance on AI tools, this theme risks reinforcing a binary narrative of AI displacing human educators. Recent scholarship urges a more nuanced understanding, positioning AI not as a replacement but as a partner in collaborative pedagogy (Cope et al., 2020; Kim, 2023; Kayyali, 2024; Kahila et al., 2024). For instance, AI’s 24/7 availability can complement teacher workload rather than undermine their authority, allowing instructors to redistribute time toward mentoring and individualized support. These counter-narratives complicate the fear of teacher obsolescence and highlight the potential for hybrid instructional ecosystems, where AI and educators function synergistically rather than competitively.
4.2 AI as a pedagogical double-edged sword
Students’ views of AI were characterized by ambivalence. While many appreciated the structured, resource-rich nature of AI-enhanced instruction, a significant number expressed concern about the resultant decline in teacher authenticity, spontaneity, and interpersonal engagement. The integration of AI into teaching practices appears to have improved efficiency and access to information, yet it also introduced a sense of pedagogical sterility when overused or uncritically implemented.
One participant shared, “Now the class feels like a presentation…like the teacher is just showing what the AI can do. Before, the teacher explained things in their own way. It was more real, more alive” (Participant 12, Male, 3rd year). This sentiment suggests a reduction in teachers’ creative agency, with AI shaping not only what is taught but how it is delivered.
Another student described a perceived drop in teacher effort: “Some teachers rely too much on AI. It feels lazy, like they let the software do the teaching. We miss the interaction and their stories or jokes” (Participant 4, Female, 3rd year). This quote reveals an emerging concern that AI, when used as a pedagogical crutch, may displace the relational and affective aspects that make classroom instruction dynamic and memorable.
Another student mentioned: “When AI gives examples, it’s so technical. But teachers used to tell us how they learned, what worked for them. That helped more” (Participant 17, Female, 4th year). Another one stated: “The teacher just reads AI content now. It’s like we are watching a summary instead of being taught” (Participant 8, Male, 3rd year).
Another student mentioned: “It feels like AI makes the class too serious sometimes. No more side stories or funny moments that made class fun” (Participant 12, Female, 4th year).
Students also noted that excessive use of AI creates a passive learning environment: “AI is good, but when it dominates the class, I feel like we are not learning from a person anymore. It’s like watching YouTube with commentary” (Participant 11, Male, 4th year). Here, the teacher is relegated to the role of a narrator rather than an engaged facilitator, which undermines student motivation and critical engagement. Holmes et al. (2022) contend that the future of education in AI-rich environments hinges not on automation alone but on cultivating human traits such as character, meta-learning, and emotional resilience. Their framework underscores the risk of neglecting the relational, moral, and affective dimensions of teaching in pursuit of algorithmic efficiency. This perspective complements findings from the present study, where students consistently affirmed that the most impactful educators were those who combined AI proficiency with empathy, humor, and adaptive moral judgment traits that cannot be coded into machines. A fourth participant expressed nostalgia for more personalized teaching: “My teacher used to give amazing personal examples. Now, it’s mostly screens and slides with AI-generated texts. It’s accurate but dry” (Participant 19, Female, 4th year). While accuracy and structure are benefits of AI, students evidently place equal value on emotional resonance, narrative depth, and the unpredictability that human storytelling brings to language learning.
Collectively, these responses reveal that students associate high AI usage with a loss of teacher originality and relational depth. The teacher’s pedagogical identity is at risk of becoming diluted or mechanical when over-reliant on technological tools, a finding that echoes concerns in current scholarship on teacher agency and digital mediation. Moreover, the students’ responses challenge the narrative that AI inherently enhances instruction, instead revealing that the manner of integration matters significantly. This aligns with findings by Derakhshan (2025), who underscores that while AI technologies can improve learning outcomes, they may also reduce emotional engagement if they displace authentic teacher interaction. Similarly, Mahapatra (2024) argues that when AI feedback replaces rather than complements teacher input, students may feel disconnected. These results validate the idea that teachers must remain central as ethical, emotional, and pedagogical agents within AI-enhanced learning environments.
From a theoretical perspective, this theme illustrates a clash between mechanized efficiency and human-centered pedagogy. While AI contributes to curriculum delivery, its overuse may marginalize practices rooted in empathy, improvisation, and cultural contextualization. In Pishghadam et al.’s (2022) terms, the over-instrumentalization of AI may reduce the “mirrors of power” and “investment” that teachers previously embodied, attributes now potentially displaced or refracted through technology.
This finding underscores the importance of maintaining a balance in AI integration to preserve the teacher’s performative, emotional, and contextual relevance. Students do not reject AI; rather, they expect teachers to mediate it critically and supplement it with their unique human insights. Teacher identity, therefore, is not eroded by AI per se, but reshaped through the tension between technological dependence and pedagogical authenticity.
These shifts in instructional style and relational dynamics also implicate the emotional labor and professional agency of teachers navigating AI-enhanced environments. As digital tools take on more instructional and evaluative roles, teachers may feel a loss of control or purpose, especially when their creative input or interpersonal contributions are overlooked by students. Kim (2023) suggests that emotional labor, the invisible effort teachers invest in building rapport, responding empathetically, and adapting to student needs, can be undermined by mechanized instruction. In this light, emotional resilience and reflective agency become crucial components of teacher identity in AI-mediated classrooms.
4.3 The irreplaceable human element
Despite their reliance on AI, participants repeatedly emphasized that certain qualities of effective teaching remain inherently human and irreplaceable. These include empathy, humor, cultural understanding, flexibility, and the capacity to build emotional connections, traits that students consistently identified as crucial to their language learning experiences.
One student explained, “ChatGPT gives good explanations, but it does not smile at you. It does not say, ‘Do not worry, you are doing great.’ Only a teacher can do that, and that matters more than you think” (Participant 3, Female, 4th year). This highlights the emotional reassurance that teachers provide, which is often underappreciated but foundational in sustaining learner confidence.
Another participant described a situation where their teacher demonstrated emotional responsiveness and care: “I had a breakdown last semester. My teacher saw I was stressed and let me submit late. Can AI do that? No. Teachers are human first” (Participant 15, Male, 3rd year). This sentiment reinforces the teacher’s role not only as an educator but also as a compassionate support figure capable of responding to individual student needs.
The importance of teacher personality and engagement also emerged as a point of contrast with AI: “Our teacher jokes with us in class. It makes learning fun and personal. AI is clever, but not funny. Not in the way people are” (Participant 9, Female, 4th year). Students valued humor and spontaneity, elements they perceived as uniquely human and critical to maintaining classroom motivation and rapport.
Cultural awareness and moral grounding were cited as additional dimensions of teaching that AI fails to replicate. As one participant noted, “The teacher understands our culture, our religion, our challenges. AI does not. That’s why we still need real teachers” (Participant 13, Male, 4th year). This underscores that teaching in EFL contexts, particularly in multilingual and culturally complex settings like Jordan, extends beyond delivering content to facilitating intercultural understanding. Statements from other three students supported this line of argument:
“AI can give you feedback, but it cannot ask you how your day was or why you missed class. Teachers do that” (Participant 11, Male, 3rd year). “We’re Arabs. Culture matters in how we learn. AI cannot understand our jokes or background. Teachers can” (Participant 2, Female, 4th year). “Sometimes just the way a teacher looks at you, you feel encouraged. AI does not have a face” (Participant 10, Male, 3rd year).
These findings affirm the enduring value of the teacher as a relational and affective presence in the classroom. While AI can efficiently deliver information and provide technical feedback, it lacks the emotional intelligence, cultural sensitivity, and social intuition that underpin effective pedagogy. Students not only recognized these human traits but prioritized them in describing their most impactful learning experiences.
From a theoretical standpoint, these results speak directly to the constructs of emotional investment and capital outlined by Pishghadam et al. (2022). Teachers, in the eyes of students, are not merely content experts but emotional anchors whose support fosters trust, safety, and motivation. Their identity is therefore linked not only to what they know, but to how they make students feel. The inability of AI to replicate these qualities positions human teachers as irreplaceable in the holistic development of learners.
While participants emphasized the affective and moral presence of teachers through phrases like “AI does not smile,” it is important to critically assess the underlying assumptions. Such perspectives, while emotionally resonant, risk romanticizing teacher roles and overlooking the structural burdens teachers face, including burnout, administrative overload, and inadequate institutional AI training. Affective labor, though crucial, must be contextualized within systemic constraints (Tian and Wang, 2025). Rather than idealizing empathy and humor as teacher “superpowers,” these accounts should inform more realistic and sustainable expectations of human-AI integration in EFL instruction.
These student perspectives suggest that successful integration of AI in language education must be accompanied by a reaffirmation of the teacher’s emotional and cultural role. The future of EFL instruction in AI-rich environments should not aim to replace teachers but to elevate their distinct strengths, namely, empathy, adaptability, and relational care that machines cannot mimic.
4.4 Reframing authority and expertise in the AI era
Rather than rejecting AI, many students articulated a redefined model of teacher identity, one that embraces technological fluency while preserving the humanistic, ethical, and pedagogical dimensions of teaching. Participants consistently described effective teachers not as those who resist AI, but as those who strategically integrate it while maintaining classroom authority, critical reflection, and student engagement.
One student described such a teacher: “My favorite teacher does not act like AI is her enemy. She uses it in class, but she also challenges us to think beyond what it says. That’s what makes her feel like a real expert” (Participant 18, Male, 3rd year). This framing suggests that students perceive expertise not merely in knowledge transmission, but in the ability to expand thinking beyond algorithmic output.
Another participant commented on teachers’ openness to AI, highlighting their evolving role as ethical mediators: “Some teachers are scared of AI, and that shows. Others use it to ask better questions or show us how to use it ethically. That’s leadership” (Participant 8, Female, 4th year). This indicates that students increasingly value critical digital pedagogy, where educators not only use AI but model responsible engagement with it.
A third participant emphasized the importance of teacher oversight: “When my teacher used AI for group projects, we saw it as a tool. But he was still in control. He made us think and reflect, not just copy” (Participant 1, Male, 3rd year). This sentiment reinforces the teacher’s role as an orchestrator of learning rather than a passive conveyor of content. The teacher’s expertise is thus redefined as the ability to transform AI into a dialogic resource rather than a substitute authority.
Finally, one student reflected on changing teacher-student dynamics in AI-integrated classrooms: “The best teachers now are like guides, not bosses. They know AI, but they also know us” (Participant 6, Female, 3rd year). Here, effective teaching is characterized by relational depth, digital literacy, and adaptive leadership. Another student mentioned “A teacher who uses AI wisely looks smarter, not weaker. It’s about how they control it” (Participant 18, Female, 4th year). Another participant mentioned: “The best teachers use AI to help us, not to replace effort. We trust them more when they use both well” (Participant 6, Male, 3rd year).
These insights collectively suggest a shift in how students conceptualize educational authority. Rather than relying solely on static knowledge, students now associate effective teaching with the ability to navigate complexity, blending human insight with technological affordances. This aligns with Pishghadam et al.'s (2022) concept of capital, particularly emotional, symbolic, and digital capital, which are increasingly central to how students evaluate their instructors. Furthermore, this reframing of expertise challenges deficit-oriented narratives around AI adoption in education. Instead of portraying teachers as being at risk of obsolescence, students viewed them as evolving professionals whose authority can be enhanced through strategic AI integration. Teachers who adapt thoughtfully, not reactively, are seen as more legitimate, credible, and inspirational in the modern EFL classroom.
In this light, AI is not a threat to teacher identity but a catalyst for its evolution. Students’ expectations reflect an emerging pedagogical paradigm in which human and machine roles are not mutually exclusive but co-constitutive. The teacher is no longer the gatekeeper of all information, but a curator of experience, a mediator of meaning, and a model of ethical digital citizenship. Although Pishghadam et al.’s identity framework offers valuable constructs such as emotioncy and capital, its application to AI-mediated classrooms requires further scrutiny. The framework primarily addresses human-human interactions and does not fully theorize the pedagogical implications of algorithmic actors or the redistribution of authority in digital contexts. For example, the concept of “mirrors of power” presupposes human intentionality, which may not translate seamlessly when mediated by non-sentient tools. Future adaptations of this model may need to incorporate AI agency, data-driven pedagogy, and ethical design to remain applicable in evolving educational ecologies.
The findings of this study resonate with Ghiasvand and Seyri’s (2025) assertion that AI integration is prompting a redefinition of language teacher identity, where instructors are increasingly viewed as facilitators, collaborators, and reflective practitioners rather than sole knowledge transmitters. While the present research focuses on students’ perceptions of these identity shifts, Ghiasvand and Seyri offer a complementary perspective by centering on teachers’ lived experiences of professional transformation in AI-mediated environments. Together, these insights suggest a reciprocal awareness between learners and educators regarding the evolving nature of teacher roles. Notably, both studies highlight a tension between technological efficiency and the enduring value of human attributes, reinforcing the need for balanced AI integration that supports, rather than supplants, the relational and cultural dimensions of language education.
These findings align with Kabadayı’s (2024) study, which revealed that teachers who are more familiar with AI and perceive it as useful report a stronger, more positive professional identity. From the students’ perspective, as reflected in this study, educators who skillfully integrate AI are seen not only as more competent but also as more relatable and forward-thinking. This alignment between teacher self-perception and student evaluation underscores that AI integration, when guided by pedagogical intent can reinforce, rather than diminish, the teacher’s authority and identity. Such evidence supports a dynamic model of identity formation in which digital fluency and human connection coexist as essential facets of 21st-century teaching.
5 Conclusion and recommendations
This study explored how undergraduate EFL students at the University of Jordan perceive the impact of AI integration on the professional identity of their language teachers. The findings reveal a nuanced landscape in which students neither reject nor uncritically embrace AI. Instead, they offer thoughtful reflections on the evolving dynamics of teacher authority, relational engagement, and instructional value in technology-rich environments.
The four emergent themes, diminished centrality of the teacher, AI as a pedagogical double-edged sword, the irreplaceable human element, and reframing of authority, collectively illustrate a shifting identity framework for EFL educators. Students recognize the efficiency and utility of AI tools, yet they remain firmly anchored in their appreciation for teachers’ emotional intelligence, cultural relevance, ethical leadership, and adaptive pedagogy.
From a theoretical standpoint, these findings affirm sociocultural conceptions of identity as fluid, co-constructed, and context-dependent. They also resonate with Pishghadam et al.’s (2022) framework, particularly the dimensions of emotioncy, investment, and capital, which help illuminate the layered and evolving relationships between students, teachers, and technology. While the findings offer valuable insights into how students perceive teacher identity in AI-enhanced classrooms, they should be interpreted in light of contextual and methodological limitations, including the study’s single-site focus and qualitative design. Claims regarding “identity reconfiguration” are interpreted as emergent and situated within the scope of students’ immediate classroom experiences. As this study was cross-sectional, it cannot speak to long-term or stable identity shifts. Longitudinal or ethnographic follow-up studies would be necessary to trace how AI-induced identity negotiation evolves over time and across different institutional and policy contexts.
These findings suggest that AI, as a globalized technology, may re-inscribe postcolonial dynamics by marginalizing local pedagogical norms and amplifying Western-centric models of teacher authority and classroom interaction.
5.1 Pedagogical recommendations
• Educators should consciously emphasize elements students value most—empathy, humor, cultural awareness, and ethical engagement. These human aspects are irreplaceable and foundational to effective EFL pedagogy.
• Institutions should provide structured guidelines on AI incorporation that preserve teacher-student interaction. For example, designating AI use in drafting or brainstorming phases followed by teacher-led reflection encourages critical thinking and prevents overreliance.
• Teacher training should offer tiered professional development on AI use—from technical skills to ethical considerations. Contextualizing these modules for EFL and including local case studies enhances relevance and responsiveness to student concerns.
• Creating opportunities for students to express their experiences with AI helps guide more inclusive, dialogic pedagogy rooted in mutual understanding.
• Policies should support teachers’ evolving identity in AI-integrated environments by emphasizing autonomy, reflection, and well-being. While “well-being” was not directly mentioned by participants, their concerns about diminished emotional connection and increased reliance on automation imply the need to address emotional labor and professional sustainability in policy frameworks.
5.2 Global implications
While this study is grounded in the Jordanian EFL context, some of its themes, such as the tension between human empathy and algorithmic instruction, or the shifting locus of authority, resonate with findings from other international studies (e.g., Mahapatra, 2024; Holmes et al., 2022). However, we do not claim that Jordanian student perspectives are universally representative. Rather, they offer situated insights that may be relevant in contexts where AI integration is accelerating without fully addressing relational pedagogy or multilingual realities.
The themes of teacher de-centering, overreliance on AI, and the preservation of human empathy have also surfaced in geographically diverse settings, including higher education systems in North America (Mollick and Mollick, 2023), South Asia (Mahapatra, 2024), and the Arab Gulf (Alotaibi and Alshehri, 2023), though with varying institutional conditions and cultural norms. Comparative studies are needed to validate or challenge the transferability of these student insights. Teachers around the world face pressure to adapt to AI tools while maintaining the relational and moral core of their profession. These findings support a growing body of international scholarship urging education systems to invest not only in technology but in the human capital that sustains it (Kim, 2023; Alexander and Belloni, 2024; Katsamakas et al., 2024). This perspective is further supported by recent empirical studies. For example, Alotaibi and Alshehri (2023) reviewed AI-based learning in Saudi higher education, revealing institutional enthusiasm tempered by a clear need for professional development and ethical guidelines. These insights affirm that AI integration is not merely a technological issue, but a pedagogical and policy-driven challenge with global implications for sustaining the human dimension in teaching.
Moreover, these findings align with recent studies that underscore the growing emphasis on learner autonomy and identity negotiation in language education. Almashour and Davies (2023) demonstrate how explicit strategy instruction fosters independent learning and shifts students’ reliance away from traditional teacher-led models, mirroring how AI use is transforming the student-teacher dynamic by encouraging more autonomous engagement. In a complementary line of inquiry, Almashour (2024) shows how translanguaging practices in EFL writing classrooms empower learners to assert their identities and develop a stronger sense of agency. These studies collectively reinforce the argument that both AI integration and pedagogical approaches supporting learner autonomy are redefining teacher identity. As students become more self-directed and emotionally invested in learning tools and multilingual practices, the teacher’s role increasingly centers on facilitating, mentoring, and navigating complex sociocultural interactions, functions that AI alone cannot fulfill.
Importantly, the reframing of authority and expertise applies across linguistic and cultural boundaries. As AI becomes increasingly multilingual and globally deployed, the question of how teacher identity transforms in diverse sociocultural landscapes becomes essential. This study contributes to that conversation by offering an EFL perspective grounded in the Arab world, with implications for global models of teacher development, digital ethics, and inclusive pedagogy.
These findings also echo the conclusions of Sharadgah and Sa’di (2022), whose systematic review of AI in ELT emphasized both the pedagogical benefits and the current limitations of AI implementation. Their review highlights that while AI tools enhance writing, speaking, and personalization of learning, there is still a critical gap in addressing emotional intelligence, teacher-student rapport, and the contextualization of instruction, concerns that align directly with the students’ perceptions in this study.
5.3 Research recommendations
• Future studies should include longitudinal and cross-cultural perspectives to examine how teacher identity transformation unfolds over time and across varied EFL contexts.
• Comparative studies involving teachers’ and students’ perspectives would provide a more holistic view of identity negotiation in AI-enhanced classrooms.
• Quantitative validation of themes emerging from qualitative studies can further strengthen generalizability and offer scalable insights for curriculum design and teacher training.
In conclusion, the integration of AI in language education is not simply a technological shift, it is a profound pedagogical and identity reconfiguration. EFL teachers are not becoming obsolete; they are being called to evolve, to lead with empathy, to integrate technology with wisdom, and to co-construct learning environments where human and artificial intelligence complement rather than compete.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
The studies involving humans were approved by The University of Jordan Ethics Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
MA: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Writing – original draft, Writing – review & editing. HA: Conceptualization, Data curation, Project administration, Resources, Supervision, Writing – review & editing. MJ: Formal analysis, Methodology, Supervision, Validation, Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research and/or publication of this article.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The authors declare that no Gen AI was used in the creation of this manuscript.
Publisher’s note
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Keywords: teacher identity, artificial intelligence, EFL education, student perceptions, digital pedagogy, Jordan, sociocultural theory, emotioncy
Citation: Almashour M, Aldamen HAK and Jarrah M (2025) “They know AI, but they also know us”: student perceptions of EFL teacher identity in AI-enhanced classrooms in Jordan. Front. Educ. 10:1611147. doi: 10.3389/feduc.2025.1611147
Edited by:
Jihea Maddamsetti, Old Dominion University, United StatesReviewed by:
Tommy Hastomo, STKIP PGRI Bandar Lampung, IndonesiaFidelis Chosa Kastuhandani, Sanata Dharma University, Indonesia
Nehme Safa, Saint Joseph University, Lebanon
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*Correspondence: Mohamad Almashour, bV9hbG1hc2hvdXJAanUuZWR1Lmpv