Learner-Centred Use of Artificial Intelligence in English Language Education:

Fifty Ways AI Can Support Learning in ESOL, Functional Skills, EFL and ESP Contexts

Author note

Shams Bhatti is an English language educator and curriculum specialist based in the UK, with extensive experience across ESOL, EFL, ESP and Functional Skills in further education, universities and industrial training contexts. He works with learners from diverse educational, linguistic and cultural backgrounds and has a professional interest in inclusive, ethically framed uses of educational technology. This article reflects practitioner-led analysis of how artificial intelligence can be used as a learning resource to support language development, confidence and learner autonomy while maintaining clear pedagogical boundaries, assessment integrity and safeguarding principles.

Abstract

The rapid expansion of generative artificial intelligence (AI) has reshaped discussions about English language education. While early discourse has primarily focused on teacher productivity, academic misconduct and assessment risk, comparatively little attention has been paid to the learner’s perspective, particularly within adult ESOL and Functional Skills provision in the UK. This article argues that, when framed as a learning resource rather than an authority, AI can support language development, learner autonomy, confidence and inclusion across ESOL, Functional Skills, EFL and ESP contexts. Drawing on current research, sector guidance and practitioner insight, the article examines learner diversity, personality, digital access, ethical constraints and assessment-for-learning principles. It proposes fifty learner-centred uses of AI and advances a clear position: AI is most educationally valuable when used as a transitional scaffold that prepares learners for independent, human communication rather than replacing it.

1. Introduction

UK English-language classrooms, particularly in further education and adult provision, are marked by profound and often underestimated diversity. Learners differ not only in age and educational history, but also in confidence, cultural expectations, familiarity with formal study and readiness to communicate publicly in a second or additional language. In many adult ESOL classrooms, it is not uncommon to find learners who are confident and articulate orally yet reluctant to write, alongside others who write competently but avoid speaking altogether.

Access to technology further complicates this picture. While some learners demonstrate advanced digital skills, many rely exclusively on mobile phones, often older models with limited operating-system compatibility. Others actively resist digital tools, associating them with past educational failure rather than opportunity. In such contexts, simplistic narratives that frame artificial intelligence as either a threat to learning or a universal solution are pedagogically inadequate and risk obscuring the realities of adult language education.

This article, therefore, adopts a learner-centred, practice-informed perspective, examining how learners themselves may use AI ethically, critically and selectively to support language learning. AI is positioned not as an assessor, content authority or shortcut, but as a mediating tool that can support rehearsal, reflection, noticing and confidence-building when used with clear boundaries and purpose. While the article foregrounds the realities of adult ESOL and Functional Skills provision, its arguments remain relevant to EFL and ESP contexts.

2. Learner Diversity and Access to AI

2.1 Digital inequality and mobile-first realities

In UK ESOL and Functional Skills provision, access to AI is overwhelmingly mobile-first rather than device-neutral. Many learners do not own laptops or tablets and instead rely on smartphones or ageing devices they have used for years. Access to AI is therefore shaped less by motivation or willingness to engage than by operating system compatibility, hardware constraints, and platform fragmentation.

In practice, this means that learners may be able to access one AI tool but not another, or may lose access following routine software updates. These limitations are rarely visible to teachers and are often internalised by learners as personal inadequacy rather than structural constraint. For adult learners who already lack confidence in formal education spaces, this can have a disproportionate psychological impact.

Ethical and inclusive AI integration must therefore be tool-agnostic and flexible, prioritising learning functions over specific platforms. AI use should remain optional rather than assumed, with whole-class, projected AI activity providing a compensatory mechanism that ensures participation without requiring individual device access.

2.2 Learning preferences and multimodal support

AI’s capacity to present information in multiple modes offers clear advantages in mixed-ability classrooms. Auditory learners benefit from spoken-style explanations, visual learners from structured exemplification and patterning, while read-and-write learners benefit from rewriting, summarising and comparative analysis. Importantly, AI allows learners to request explanations in forms that suit them, shifting a degree of control towards the learner without displacing the teacher’s pedagogical role.

2.3 Learners with limited study skills

Many adult learners enter ESOL and Functional Skills provision with limited experience of independent study due to interrupted education or negative prior schooling. AI can act as a study-skills mediator, helping learners interpret task demands, break activities into manageable stages and understand success criteria. Used in this way, AI extends teacher support beyond the classroom rather than replacing it, particularly for learners who lack academic cultural capital.

3. Personality, Affect and Confidence

Personality and affective factors exert a powerful, and sometimes underestimated, influence on language learning. Shy learners, those culturally conditioned to avoid public error and learners with low confidence often underperform despite possessing adequate linguistic knowledge. For these learners, the primary barrier is rarely competence but emotional readiness.

AI can offer a private, non-judgemental rehearsal space in which learners can practise language, test hypotheses, and clarify understanding without the immediate pressure of peer evaluation. Crucially, however, the pedagogical objective is not concealment but transition. AI should function as a temporary scaffold, supporting movement from private rehearsal to pair work, group discussion and eventually public communication. When this trajectory is made explicit, AI use becomes a confidence-building bridge rather than a place of retreat.

Overconfident learners may also benefit from AI use, as it can serve as a neutral reference point that highlights gaps in accuracy, coherence, or register without personal confrontation. Highly motivated learners, meanwhile, can use AI for extension, enrichment and autonomous exploration, particularly in mixed-ability classrooms where teacher time is necessarily finite.

4. Projected AI and Assessment for Learning

In classrooms equipped with smart boards or projectors, AI can be used as a shared cognitive artefact rather than an individual tool. When prompts are teacher-controlled and content anonymised, projected AI output enables collaborative analysis of language, genre and task fulfilment. Learners can evaluate AI-generated responses against assessment criteria, identify weaknesses and propose improvements.

This constitutes assessment for learning, not assessment by AI. AI must not grade learners or generate marks, particularly in high-stakes contexts. Teacher judgement remains central, with AI functioning as a stimulus for metacognitive discussion, critical language awareness and collective problem-solving.

5. Privacy, Ethics and Safeguarding

AI raises legitimate concerns regarding learner privacy, particularly for vulnerable adult learners. Risks arise when personal data is entered or when individual learners’ work is publicly displayed. These risks can be mitigated through anonymisation, fictionalisation, explicit learner guidance and teacher mediation.

Paradoxically, AI can also enhance psychological privacy by allowing learners to practise, ask questions and make mistakes without public exposure. Ethical AI use, therefore, depends less on the technology itself than on pedagogical design, transparency and professional judgement.

It is also important to acknowledge limitations. AI systems are not transparent in their training data, may produce fluent but inaccurate responses and are unevenly accessible across devices and platforms. Without careful mediation, AI use can unintentionally privilege learners with higher digital confidence or reinforce surface-level engagement with language. AI should therefore be approached as a conditional and bounded resource, not a neutral intervention.

6. Fifty Learner-Centred Uses of AI as a Learning Resource

A. Reading

1. Linguistic mediation of complex texts

2. Graduated summarisation across CEFR levels

3. Self-generated comprehension questioning

4. Cultural and pragmatic clarification

5. Transformation of texts into practice tasks

   
   

B. Writing

6. Pre-writing conceptual scaffolding

7. Reflective post-writing feedback

8. Syntactic and structural variation

9. Register and genre modulation

10. Comparative noticing through revision

    
    

C. Speaking

11. Private rehearsal of spoken interaction

12. Structured spoken exam preparation

13. Acquisition of formulaic language

14. Discourse-level presentation planning

15. Critical analysis of model responses

    
    

D. Listening

16. Exposure to spoken-style texts

17. Adjusted speech rate and complexity

18. Note-taking practice

19. Schema activation and prediction

20. Transcript-supported reflection

    
    

E. Grammar

21. Personalised grammatical explanation

22. Targeted remedial practice

23. Contrastive grammar analysis

24. Low-stakes diagnostic testing

25. Reflective error analysis

    
    

F. Vocabulary

26. Thematic lexical expansion

27. Collocational competence

28. Contextualised phrasal-verb practice

29. Semantic nuance and synonym control

30. Systematic recycling and retrieval

    
    

G. Functional Language

31. Simulation of authentic written tasks

32. Controlled form-filling practice

33. Problem-solving discourse rehearsal

34. Everyday spoken interaction practice

35. Pragmatic appropriacy analysis

    
    

H. English for Specific Purposes

36. Sector-specific lexical development

37. Workplace dialogue rehearsal

38. Mediation of technical documents

39. Professional presentation preparation

40. Development of professional linguistic identity

    
    

I. Metacognition and Confidence

41. Goal-setting and progress monitoring

42. Reflective learning dialogue

43. Individualised clarification of gaps

44. Anxiety reduction through rehearsal

45. Transition to classroom participation

    
    

J. Exam Preparation

46. Familiarisation with task formats

47. Criteria-based answer evaluation

48. Strategic revision planning

49. Time-management rehearsal

50. Reflective performance evaluation

7. Promoting AI Use Without Creating Dependence

AI dependence cannot be prevented through prohibition; it can only be prevented through intentional pedagogical design. Learners are most likely to become dependent on AI when it is framed as a source of answers rather than a support for thinking. Reframing AI as a process tool rather than a product generator is therefore essential.

Models such as Draft–Distance–Do introduce purposeful separation between AI-supported preparation and independent performance. When combined with transparency about AI use, reflective discussion and the gradual withdrawal of scaffolding, these approaches reinforce learner autonomy. Above all, communicative use of language with other people must remain the explicit end point of AI-supported learning.

8. Conclusion

This article has argued that artificial intelligence, when used by learners as a mediated learning resource, can support linguistic development, confidence, autonomy and inclusion across ESOL, Functional Skills, EFL and ESP contexts. Its value lies not in efficiency or automation, but in its capacity to scaffold readiness for independent communication.

AI is neither neutral nor inevitable in its effects. Without ethical framing and pedagogical intent, it risks reinforcing passivity, dependency and inequality of access. Used reflectively and selectively, however, it can reduce barriers created by confidence, educational disadvantage, age and digital exclusion. AI should therefore be understood not as a destination, but as a pedagogical bridge—one that supports learners in moving from rehearsal to participation, from uncertainty to agency and from silence to voice.

The responsibility for this framing rests unequivocally with educators and institutions. AI benefits learners not because it is intelligent, but because it is bounded by professional judgement, ethical constraint and a sustained commitment to inclusive education.

References

British Council (2024) Artificial intelligence and English language teaching: Preparing for the future. London: British Council.

Creely, E., Barnes, M. and Tour, E. (2025) ‘Adult EAL learners’ attitudes to generative AI’, ReCALL, 37(1), pp. 1–17.

Department for Education (2024) Generative AI in education: Educator and expert views. London: DfE.

Jisc (2024) Generative AI student guidance for further education. Bristol: Jisc.

Jisc (2025) Student perceptions of artificial intelligence. Bristol: Jisc.

Li, B. (2024) ‘A systematic review of ChatGPT in language learning’, Computers and Education: Artificial Intelligence, 6, 100123.

Li, B. et al. (2025) ‘Generative AI research in language education’, Computers and Education: Artificial Intelligence, 9, 100445.

Lo, C.K. (2024) ‘ChatGPT in ESL/EFL education’, Smart Learning Environments, 11(8).

Mahapatra, S. (2024) ‘Impact of ChatGPT on ESL writing’, Smart Learning Environments, 11(5).

UNESCO (2023). Guidance for generative AI in education and research. Paris: UNESCO

© Shams Bhatti, Keystone Academic Consultants Ltd. All rights reserved.

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