Tech-Integrated Pedagogical Framework for Enhancing Learning with Artificial Intelligence in ESP Classrooms - ECEIA 26 12th European Conference on Ethics and Integrity in Academia

Synopsis

This interactive workshop presents a research-informed pedagogical framework for strategically integrating Large Language Models into ESP engineering classrooms. Based on three years of classroom research in Japan, participants will explore the ethical and practical applications of AI through industry-standard prompting frameworks and custom chatbot integration, leaving with concrete strategies to transform student-AI interaction from ad hoc experimentation into authentic, rigorous language learning.

Abstract

1. Introduction, Context, and Background
The accessibility and rapidly changing affordances of Large Language Models (LLMs) present significant challenges for English for Specific Purposes (ESP) instruction, but also tremendous opportunities for learning. Since before the release of Eaton`s (2023) Handbook of Academic Integrity, general educators have fallen behind due to a lack of pedagogical frameworks to move beyond the generic use of technology. Recently, with the widespread use of AI by students, educators face the ethical challenge of how to enhance learning while continuing to develop discipline-specific language acquisition, critical thinking, and higher-order thinking skills (Jose et al., 2025). This workshop, grounded in three years of applied classroom research in Japanese engineering classrooms, explores these issues and offers a framework based on applied tech-integrated TPACK (Dusza, 2023; Koehler & Mishra, 2014).

2. Relevant Conference Theme(s)
This contribution directly addresses the themes of
● Education & training in the area of ethics and integrity (Innovative experiences and research on education and training),
● Ethical teaching of sensitive topics in the age of AI - Educating students about censorship and hallucinations by AIs when studying, and
● Academic integrity in hybrid and online education - Examining and treating new forms of misconduct in digital learning spaces, proctoring ethics, and balancing student privacy with assessment integrity.

3. Aims and Objectives
This workshop focuses on practical, ethical applications of AI tools within a defined pedagogical context to foster authentic learning outcomes using industry-standard prompting patterns and processes. The workshop aims to equip ESP practitioners with a structured approach to using LLMs and deliver the following objectives.

(1) Provide clarity on the capability of AI and the current affordances of popular LLMs.

(2) Present, explain, and show example applications of two industry-standard Prompt frameworks supported with examples of teacher and student material for assisting learning.

(3) Present the Dusza (2023) adapted tech-integrated TPACK framework, using AI, explaining how it enhances language contact and explicit understanding.

(4) Provide participants with access to custom AI Chatbots and provide "new ways to think", regarding how students work with AI in the learning process, and how to collect this learning data, with the current limitations we have on privacy, and using third party softwar in classrooms.

4. Brief Description of Approach/Methods
The presented research employed an iterative, design-based methodology. The tech integration framework (Dusza, 2023) was implemented and refined across multiple cohorts of engineering students. Data collection included analysis of student-AI interaction logs, comparative assessment of writing outputs (with and without structured AI guidance), and reflective student feedback. This practice-led research directly informs the workshop’s practical components.

5. Main Findings and Lessons Learnt
Findings indicate that structured, scaffolded use of LLMs—as opposed to ad-hoc student experimentation—leads to markedly superior outcomes. Specifically, students produced more authentic, technically accurate, and rhetorically effective engineering documents. A key lesson was that explicit training in “prompt engineering” transforms the LLM from a potential shortcut into a collaborative tool for genre analysis, drafting, and revision, thereby preserving critical learning processes.

6. Target Audience
This workshop is designed for educators, curriculum developers, and educational leaders seeking to strategically and ethically implement AI tools within their professional and academic English programs. No advanced technical or pedagogical expertise is required.

7. Conference Workshop Activities
This is an interactive discussion and workshop that provides ample hands-on opportunities for participants. Attendees will:

● Engage critically with the core principles of AI, LLM, and pedagogical frameworks presented.
● Discuss applications of prompting models and how they are useful in classrooms, lesson design, preparing materials, assessment, and other educational issues
● Work in teams to collaboratively conceptualise integrating custom AI chatbots in their material, and how to automate ipsative and progressive assessments, and
● discuss solutions on implementing takeaways in our corners of the globe, where educational practices and ethics are different.

8. Notes on Ethical Approval
The workshop will emphasize the ethical and practical dimensions of AI, LLMs, and Chatbots in the learning process, and how they can be undeniably superior to what we are currently experiencing: untrained students using AI at whim.

Keywords

English for Specific Purposes; Large Language Models; Prompt Engineering; Learner-Centred Pedagogical Framework; Academic Integrity; TPACK

References

Dusza, D. G. (2023). Machine translation in the writing process: Pedagogy, plagiarism, policy, and procedures. In S. E. Eaton (Ed.), Handbook of Academic Integrity (2nd ed., pp. 1487–1509). Springer. https://doi.org/10.1007/978-981-287-079-7_152-1

Eaton, S. E. (Ed.). (2023). Handbook of academic integrity. Springer Nature. https://doi.org/10.1007/978-3-031-39989-3

Jose, B., Cherian, J., Verghis, A. M., Varghise, S. M., Mumthas, S., & Joseph, S. (2025). The cognitive paradox of AI in education: Between enhancement and erosion. Frontiers in psychology, 16(1), 1550621. https://doi.org/10.3389/fpsyg.2025.1550621

Koehler, M. J., & Mishra, P. (2014). Introducing TPACK. In Handbook of technological pedagogical content knowledge (TPCK) for educators (pp. 13-40). Routledge.