By

Oghenetega Ferife Nwokoro

nwokoro.oghenetega@dou.ed.ng

07067001452

1. Introduction

English language teachers face mounting pressure to remain current in a rapidly evolving technological landscape. The integration of digital tools — including language learning applications, AI-powered platforms, and interactive media — holds significant potential for enhancing student engagement, personalising instruction, and streamlining lesson planning (Abdunnazar, 2024). English is widely recognised as a global lingua franca, serving as the official language of communication in over 88 countries (Bridge Education Group, as cited in this study); in Nigeria, it functions as both a second language and the medium of instruction across educational levels, underscoring the critical importance of equipping teachers to deliver it effectively.

The rapid pace of technological change, culminating in the widespread adoption of Artificial Intelligence (AI), demands a corresponding evolution in pedagogical practice. AI systems can provide interactive feedback, support differentiated instruction, and connect teachers to global communities of practice — yet many English language teachers remain underprepared to leverage these tools (Pazilah, Haslim, & Yunus, 2019). As Chia (2024) notes, innovative technology in education is fundamentally about reimagining established pedagogies rather than merely supplementing them.

Despite growing scholarly interest in technology-enhanced language teaching, there is limited empirical research examining the awareness, access, perceptions, and challenges facing English language teachers in the Nigerian context specifically. This study addresses that gap by exploring how emerging technologies can be harnessed to build teachers' professional literacy skills in the era of AI, drawing on connectivism as its theoretical anchor. The findings offer practical insights for institutional policy, teacher training, and curriculum development.

2. Theoretical/Conceptual Framework

2.1 Connectivism

The theoretical framework underpinning this study is connectivism, a learning theory developed by George Siemens and Stephen Downes in the mid-2000s. Connectivism posits that learning occurs through the formation of connections across diverse networks of nodes — where a node may be a person, a resource, a webpage, or a digital platform (Siemens, 2008). Unlike earlier cognitive or behaviourist theories, connectivism explicitly foregrounds technology as a central mediator of knowledge acquisition, making it particularly well suited to studies of digital and AI-enhanced education.

Central to connectivism is the principle that maintaining and nurturing connections is more critical than the accumulation of isolated knowledge, and that the capacity to know more is more important than what is currently known. This has direct relevance to English language teachers, who are conceptualised in this study as 'nodes' that must actively connect to emerging technologies, online professional communities, and up-to-date pedagogical resources in order to remain effective in their practice.

It is acknowledged that connectivism is not without its critics. Kerr (2007) and Verhagen (2006) have questioned whether it constitutes a fully distinct learning theory or rather a pedagogical perspective, and scholars have noted that its principles can be difficult to operationalise systematically in classroom contexts. Complementary frameworks — including Mishra and Koehler's (2006) Technological Pedagogical Content Knowledge (TPACK) model and the European DigCompEdu framework (Redecker, 2017) — offer structured competency models for teacher digital literacy that could productively extend the theoretical grounding of future research in this area. Nevertheless, connectivism's emphasis on networked, technology-mediated learning renders it an appropriate and illuminating lens for the present study.

2.2 Key Conceptual Definitions

i. Technology

For the purposes of this study, technology refers to methods, systems, and devices arising from the application of scientific knowledge for practical purposes (Alqahtani, 2019). In the ELT context, this encompasses language learning applications, online platforms, audio-visual tools, and AI-powered systems that extend the teacher's role as a facilitator of learning. Bransford and Cocking (2000) argue that computer technology enables teachers and learners to forge connections that transcend local boundaries, expanding opportunities for enriched, collaborative engagement.

ii. Artificial Intelligence in Education

Artificial Intelligence refers to computer systems capable of mimicking aspects of human intelligence, including natural language processing, pattern recognition, and adaptive feedback (Bit, Biswas, & Nag, 2024). In the educational context, AI tools support administrative efficiency, personalised instruction, and real-time learner assessment. Notably, platforms such as Duolingo deploy AI systems to analyse learner proficiency and adapt content dynamically to meet individual needs (Bicknell et al., 2023). However, it is equally important to acknowledge the limitations and risks associated with AI in education, including concerns about algorithmic bias, data privacy, and the potential for AI to reinforce existing inequalities if access is not equitable (cf. Selwyn, 2019).

iii. Emerging Technologies in ELT

Emerging technologies are characterised as innovations that build upon and extend prior technological advancements, encompassing both content production and content delivery modalities (Olivia et al., 2019). In the ELT sphere, these include computational tools, mobile devices, virtual learning environments, and AI-enabled platforms. Abrahams (2015, as cited in Journal of Curriculum, 2022) notes that emerging technologies facilitate the generation, sharing, and preservation of academic resources, enabling more stimulating and personalised learning experiences.

3. Literature Review

3.1 Impact of AI on English Language Education

The literature consistently highlights the transformative potential of AI for English language education. Bit, Biswas, and Nag (2024) argue that AI redefines personalised learning experiences through intelligent tutoring systems, offering enhanced student engagement and adaptive feedback. AI tools support English language teachers in several practical ways: they automate administrative tasks such as marking, provide immediate diagnostic feedback on student performance, deliver up-to-date teaching materials, and enable the creation of engaging, differentiated learning environments.

Motteram, Onat-Stelma, and Slaouti (2008) documented the growing use of technology in ELT surveying contexts, while more recent scholarship — including Abdunnazar (2024) and Chia (2024) — emphasises the need for pedagogically grounded approaches to technology integration that move beyond tool adoption towards genuine reimagining of teaching practice. Anne and Mahalashmi (2014) note that technology enables individualised learning by accounting for students' varying aptitudes and interests, underscoring the importance of teacher readiness to exploit these affordances.

3.2 Emerging Technologies for Teacher Professional Development

Several specific technologies have demonstrated value in building English language teachers' professional literacy. Audio recording tools and podcast platforms support oral skills development and enable teachers to model correct pronunciation and comprehension (Oral Skills category, this study). Video conferencing platforms such as Zoom and Google Meet have proven particularly valuable in contexts where English is a second language, facilitating direct interaction with proficient speakers and supporting pronunciation practice (Barone & Wright, 2020). AI chatbots provide individualised student support and allow teachers to track learning progress systematically (Dipanwita et al., 2024).

Collaborative platforms including Moodle, wikis, and blogs promote reading and writing engagement, allowing students to contribute to and extend shared academic texts (Amin, 2019; Warschauer & Liaw, 2010). These tools collectively extend the teacher's facilitative role beyond physical classroom constraints, addressing access inequities and enabling continuous professional development.

3.3 Challenges and Ethical Considerations

Despite their potential, the adoption of AI and emerging technologies in ELT is not without challenges. Access inequalities remain a significant barrier, particularly in lower-resourced settings; without reliable infrastructure, even highly motivated teachers cannot integrate AI tools effectively. Institutional and governmental support is frequently identified as insufficient (Pazilah et al., 2019), and the absence of sustained professional development leaves many teachers ill-equipped to move beyond surface-level tool use.

Ethical considerations are also salient. Questions of data privacy, the transparency of AI algorithms, and the potential for bias in AI-generated feedback must be addressed in any institutional adoption framework. Furthermore, teacher agency in technology adoption — ensuring that teachers are empowered collaborators rather than passive recipients of technological mandates — is essential to sustainable integration (Selwyn, 2019).

4. Methodology

4.1 Research Design

This study adopted a descriptive survey design to gather information on English language teachers' awareness, usage, perceptions, challenges, and proposed solutions related to emerging technologies in ELT. A descriptive survey approach is appropriate for studies seeking to characterise the attitudes and behaviours of a defined population at a given point in time (Ajayi & Abanobi, 2016). The study should be understood as an exploratory pilot investigation; findings are intended to generate hypotheses and inform the design of larger-scale research rather than to support definitive generalisations.

4.2 Sampling and Participants

A structured questionnaire was distributed online via Google Forms to English language teachers across primary schools, secondary schools, and tertiary institutions in Nigeria. Participants were recruited through convenience and snowball sampling via professional networks; a total of 53 completed responses were received. The sample comprised 5 primary school teachers (9.4%), 25 secondary school teachers (47.2%), 24 tertiary institution lecturers (45.3%), and 2 respondents in other categories (3.7%). Educational qualifications ranged from B.A. and B.Ed. to M.Ed., M.A., Ph.D., and PGDE, with the majority holding postgraduate qualifications (see Table 1 and Table 2). Teaching experience ranged from below five years to over 21 years (see Table 3).

The sample size of 53 is acknowledged as a significant limitation of this study (see Section 4.5). Regional coverage, precise institutional distribution, and response rate are not fully documented, and readers should exercise caution in generalising findings beyond the contexts represented in this sample.

4.3 Instrument

The questionnaire was designed in two sections. Section 1 collected demographic and professional background data (teaching level, educational qualifications, teaching duration). Section 2 comprised Likert-scale and checklist items covering five thematic areas: (1) awareness of emerging technologies and AI tools in ELT; (2) teachers' perceptions of AI and emerging technology; (3) access to and availability of technology tools; (4) challenges encountered in adopting AI in ELT; and (5) possible solutions to promote technology use. Items were developed by the researcher drawing on relevant literature and were reviewed by subject-matter peers prior to distribution. Future research should include formal piloting and reporting of internal consistency measures such as Cronbach's alpha.

4.4 Data Analysis

Data were analysed using descriptive statistics (frequency counts and percentages) to summarise patterns of response across the five thematic areas. Results are presented in tabular form with interpretive commentary. While the descriptive approach provides a useful overview of teachers' profiles and perspectives, it is recognised that inferential analysis — including chi-square tests of association to explore relationships between, for example, qualification level and AI readiness, or access and teaching context — would strengthen the analytical contribution of the study. Such analyses are recommended for follow-up research with a larger sample.

4.5 Limitations

Several limitations should be noted. First, the sample size (n = 53) is small and was obtained through convenience sampling, which limits representativeness and generalisability. Second, the questionnaire relies on self-reported data, which is subject to social desirability bias; teachers may overestimate their familiarity with or readiness to adopt AI tools. Third, instrument validation was informal, and reliability statistics are not available for this study. Fourth, the absence of qualitative data (e.g., open-ended responses or follow-up interviews) means that the underlying reasons for observed patterns — such as why some teachers resist AI adoption or how specific access barriers manifest in practice — cannot be fully explored. These limitations notwithstanding, the study provides a useful exploratory snapshot of ELT teacher perspectives on emerging technology in the Nigerian context.

Ethical considerations for this study include the voluntary nature of participation, the anonymity of respondents, and the absence of sensitive or identifying data collection. Future studies utilising AI tools in research or classroom contexts should additionally address data protection regulations, the transparency of algorithmic systems, and mechanisms to safeguard teacher and learner agency.

5. Results

5.1 Participant Demographics

Table 1 presents the distribution of respondents by teaching level. The majority were secondary school teachers (47.2%, n = 25) and tertiary institution lecturers (45.3%, n = 24), with primary school teachers accounting for 9.4% (n = 5). This distribution reflects the convenience sampling strategy and limits the generalisability of findings to primary-level contexts.

Table 1: Teaching Level of Respondents

Table 2: Educational Qualifications of Respondents

Table 3: Teaching Duration of Respondents

5.2 Awareness of Emerging Technologies and AI Tools in ELT

Table 4 presents respondents' self-reported familiarity with five categories of emerging technology relevant to ELT. Across all five items, a majority of respondents indicated being 'Familiar' or 'Very Familiar'. Awareness was highest for English language learning mobile applications (79.2%, n = 42) and virtual learning platforms with AI features (73.5%, n = 39). Awareness of general emerging technology tools stood at 69.8% (n = 37 combining 'Very Familiar' and 'Familiar'). A notable gap was observed for apps supporting the four language communication skills, where 47.1% (n = 25) indicated slight or no familiarity, suggesting a specific area for targeted professional development.

Table 4: Awareness of Emerging Technologies and AI Tools in ELT (n = 53)

5.3 Teachers' Perceptions of AI and Emerging Technology

Table 5 presents teacher perceptions across five attitudinal statements. Across all items, the overwhelming majority of respondents expressed agreement or strong agreement. Most notably, 100% of respondents agreed or strongly agreed that teachers should be trained on how to use emerging technology (Table 7, Statement 1: SA = 43 [81.1%], A = 10 [18.8%]). Perceptions of AI's capacity to improve the four language skills were similarly positive, with 92.4% (n = 49) agreeing or strongly agreeing. A small but noteworthy minority expressed neutrality or disagreement on whether emerging technology aids cross-cultural collaboration (7.5% neutral; 2% each disagree and strongly disagree), possibly reflecting limited prior experience with global online communities.

Table 5: Teachers' Perceptions of AI and Emerging Technology (n = 53)

5.4 Access to Technology Tools

Table 6 summarises respondents' reported access to five categories of technology tools. Access to smartphones was near-universal (n = 47, 88.7%), and the majority had access to laptops or desktops (n = 39, 73.6%) and reliable internet (n = 31, 58.5%). However, access to projector or multimedia tools (n = 12, 22.6%) and AI-powered educational platforms (n = 16, 30.2%) was markedly lower, indicating a significant infrastructure gap that may constrain effective technology integration particularly for classroom-based delivery and higher-order AI applications.

Table 6: Access to Technology Tools (n = 53)

5.5 Challenges in Adopting AI and Emerging Technology

Table 7 presents the frequency of agreement with five challenge statements. Lack of access to digital tools was identified as the most widely acknowledged challenge, with 94.3% (n = 50) of respondents agreeing or strongly agreeing. Lack of continuous training (86.7%, n = 46) and lack of institutional or government support (77.3%, n = 41) were also strongly endorsed. A meaningful minority (45.2%, n = 24) expressed agreement that emerging technology creates classroom management challenges, while responses to the statement about fear of AI replacing teachers were more divided: 45.2% agreed, while 24.5% strongly disagreed and 15% disagreed, suggesting that while job displacement anxiety exists, it is not universally shared.

Table 7: Challenges of AI and Emerging Technology in ELT (n = 53)

5.6 Proposed Solutions to Promote Technology Integration

Table 8 captures respondents' endorsement of five proposed solutions. All five solutions received overwhelming support, with no respondent disagreeing with the proposals to train teachers (100% agreement) or for authorities to provide AI tools (98.1%). These findings strongly affirm a mandate for institutional action and suggest that teacher willingness to engage with emerging technology is high when structural support is in place.

Table 8: Proposed Solutions to Promote Emerging Technology in ELT (n = 53)

6. Discussion

The findings of this study paint a largely optimistic picture of English language teachers' awareness of and disposition towards emerging technologies, while also surfacing structural barriers that must be addressed for technology integration to be educationally meaningful and equitable.

The high levels of awareness reported — particularly for mobile applications and virtual learning platforms — are consistent with the broader trend of smartphone proliferation in Nigerian society (Table 6: 88.7% of respondents owned a smartphone). This resonates with the connectivist argument that everyday digital devices constitute entry points into networked, technology-mediated learning (Siemens, 2008). However, awareness alone does not translate into effective pedagogical practice; the significant gaps in access to AI-powered educational platforms (30.2%) and multimedia projection tools (22.6%) represent a structural impediment to classroom-based AI integration that policy interventions must address.

Teachers' overwhelmingly positive perceptions of AI's potential to enhance language skills (92.4% agreeing that AI tools improve listening, speaking, reading, and writing) and personalise instruction (92.4%) suggest strong motivational readiness. Yet this enthusiasm is tempered by the reality that many teachers lack the sustained professional development needed to translate positive attitudes into confident, pedagogically grounded practice. The finding that 86.7% of respondents endorsed the need for continuous training, and that 100% agreed teachers should receive training (Table 8), underscores that willing teachers are currently underserved by professional development infrastructure.

The challenges data further reveal a significant concern about institutional and governmental support (77.3% agreeing it is lacking), pointing to a systemic rather than individual failure. This aligns with Pazilah et al.'s (2019) observation that technology adoption in ESL/EFL contexts is often hindered not by teacher reluctance but by inadequate infrastructure and policy frameworks. The finding that fear of AI replacement is not universally held (30.1% neutral; 24.5% strongly disagreeing) is noteworthy and somewhat reassuring, though those who do harbour such concerns (45.2% agreeing) may benefit from professional development that reframes AI as a pedagogical complement rather than a substitute for teacher expertise.

The proposed solutions data present a clear and coherent mandate: train teachers, provide tools, and cultivate a culture of willingness and collaboration. The near-unanimous endorsement of these solutions (98–100% agreement across items) suggests that the barriers to technology integration in this context are primarily structural and institutional rather than attitudinal. This finding has important implications for how authorities and institutions should prioritise their investments.

7. Recommendations

Based on the findings and their implications, the following recommendations are organised by timeframe and stakeholder:

Short-term (0–12 months): Targeted Training and Awareness

• Educational authorities should design and deliver focused professional development workshops introducing English language teachers to specific AI tools relevant to ELT, including AI chatbots for student feedback, adaptive language learning platforms, and collaborative digital writing environments.
• Workshops should be differentiated by teaching level and qualification, and should address both technical skills and pedagogical integration — not merely tool familiarisation.
• Training should explicitly address data privacy, the ethical use of AI tools, and strategies for maintaining teacher agency in AI-mediated classrooms.

Medium-term (1–3 years): Institutional Procurement and Infrastructure

• Institutional authorities should develop procurement strategies to ensure equitable access to AI-powered educational platforms and multimedia tools, with particular attention to under-resourced primary and secondary school contexts.
• Governments should develop and fund national-level ELT technology frameworks aligned with digital competency standards such as DigCompEdu (Redecker, 2017) or TPACK (Mishra & Koehler, 2006), adapted to the Nigerian context.
• Peer-support networks — connecting teachers within and across institutions — should be established to facilitate the sharing of resources, strategies, and experiences, operationalising the connectivist principle of learning through networked community.

Long-term (3+ years): Policy and Evaluation

• Policy frameworks should mandate and fund continuing professional development in educational technology as part of teacher licensure and appraisal processes.
• Institutions should implement monitoring and evaluation mechanisms to assess the effectiveness of technology training, using metrics that capture both teacher competency development and measurable learning outcomes.
• Future research, with larger and more geographically representative samples, should employ mixed-methods designs, including inferential statistics and qualitative follow-up, to build a more comprehensive evidence base for AI integration in Nigerian ELT.

8. Conclusion

This exploratory study contributes to the growing body of evidence on technology integration in ELT by providing an empirical snapshot of Nigerian English language teachers' awareness, perceptions, access, and challenges with respect to emerging AI tools. The findings affirm that teachers are largely aware of and positive about emerging technologies, but that structural barriers — limited access, insufficient training, and inadequate institutional support — constrain the translation of readiness into practice.

Framed within connectivism, the study underscores the importance of enabling teachers, as active nodes in educational networks, to form meaningful connections with the tools, platforms, and professional communities that can sustain their development in the AI era. As Pazilah et al. (2019) observe, great teachers are not replaced by technology; rather, technology in the hands of great teachers becomes transformational. For this transformation to be realised at scale, it requires deliberate, sustained, and equitable investment from educational authorities at all levels.

This study is acknowledged as a pilot with the limitations outlined in Section 4.5. Future research should build on these findings with larger, randomised or stratified samples, validated instruments, inferential analysis, and qualitative components to deepen understanding of how English language teachers in Nigeria and similar contexts can most effectively navigate the era of AI.

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