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Designing and Implementing AI-assisted English Language Teaching
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Designing and Implementing AI-assisted English Language Teaching
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By the end of the course, students will be able to:
- Explain in English the basic concepts, features, and limitations of generative AI and LLMs, particularly in relation to English language education.
- Design and refine prompts for ELT contexts (e.g., vocabulary, reading, writing, speaking tasks).
- Propose multiple AI-based activities and materials for each of the four skills (reading, writing, listening, speaking).
- Critically evaluate AI-generated output (e.g., texts, feedback, tasks) and suggest improvements.
- Design AI-supported self-study environments for English learners and explain how they promote learner autonomy.
- Discuss ethical, privacy, and academic integrity issues related to AI use in English education and present their own informed views in English.
- Develop and present an AI-integrated lesson plan and conduct a short micro-teaching session, receiving and giving peer feedback.
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This intensive course explores how generative AI, especially large language models (LLMs), can be integrated into English language teaching and learning.
Students will:
- Learn the basic mechanisms, strengths, and limitations of generative AI.
- Experiment with prompt design in various teaching and learning contexts.
- Critically evaluate AI-generated texts, feedback, and activities
- Design and present AI-enhanced lesson plans and micro-teaching sessions.
The course emphasizes using AI critically and creatively, not passively depending on it.
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Generative AI in ELT; AI-enhanced Language Learning; Learner Autonomy with AI; Ethics and Academic Integrity in AI Use; Prompt Engineering for ELT
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| No. | 内容 | Content |
第1回
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Orientation & Introduction to Generative AI for ELT / Course overview, assessment, AI use policy; Opportunities and challenges of AI in English education / Activity: - Students generate an AI-written self-introduction and compare it with their own. / Pair discussion: - "What surprised you about the AI's output?"
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第2回
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Prompting Basics for English Learning & Teaching (Workshop) / Principles of prompt design: clarity, role specification, examples, step-by-step prompts, constraints / Activities: - Design different prompts for the same learning objective (e.g., vocabulary, pre-reading, discussion questions); - Compare AI outputs and co-create "Prompt Guidelines for ELT Teachers" in English
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第3回
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AI for Vocabulary and Reading Activities / Using AI for: - Vocabulary lists, collocations, and example sentences; - Pre-reading activities, background knowledge support / Activities: - Use AI to generate vocabulary practice and pre-reading tasks based on sample texts; - Evaluate quality and suggest improvements
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第4回
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Ethical and Pedagogical Issues in AI Use / Risks of AI for language learners: over-reliance, misinformation, privacy, academic integrity; Teacher responsibilities and classroom policies / Activities: - Small groups draft a "Classroom AI Use Policy" for high school or university students; - Share and discuss policies as a whole class
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第5回
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AI for Writing – Idea Generation and Planning / Using AI for brainstorming topics, generating viewpoints, and planning essay structure; Positioning AI as a "thinking partner," not a ghostwriter / Activities: - Work with an example writing task (e.g., opinion essay); - Use AI to generate ideas and outlines; - Compare AI-supported outline with a manually created outline and discuss pros/cons
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第6回
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AI-generated Feedback – Strengths and Pitfalls (Workshop) / Types of feedback: grammar, vocabulary, organization, content; Problems: overcorrection, misleading advice, loss of learner agency / Activities: - Use AI to generate feedback on sample essays; - Classify feedback into "useful for teachers," "useful for learners," and "potentially harmful"; - Design a simple "AI-assisted feedback workflow" for a writing class
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第7回
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AI for Speaking and Listening Support / AI chatbots as role-play partners; Using AI to create scripts and adjust difficulty for listening; Discussion of pronunciation and pros/cons of AI-based feedback tools / Activities: - Choose a scenario (e.g., airport, part-time job interview, university seminar); - Design prompts and a task sheet for learner–AI role-play; - Demonstrate and discuss classroom implementation
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第8回
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Evaluating AI Output – Critical Literacy for Teachers and Learners / Identifying bias, stereotypes, and cultural issues in AI-generated texts; Differences between "AI English" and diverse real-world English / Activities: - Analyze AI-generated teaching texts for cultural bias or unnatural discourse; - Rewrite texts and create "Guidelines for Teaching Students to Question AI Texts"
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第9回
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Task-based Language Teaching (TBLT) and AI Integration / Review of TBLT concepts: tasks, input, output, feedback; Ways to incorporate AI into task cycles / Activities: - Groups design a communicative task and decide how AI will be used before, during, and after the task; - Present and receive feedback on task designs
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第10回
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Designing AI-enhanced Activities for the Four Skills (Workshop) / Integrating listening, speaking, reading, and writing with AI support / Activities: - Each group chooses a skill pair (e.g., Reading + Writing or Listening + Speaking); - Design at least one original AI-supported classroom activity; - Mini-demonstration and discussion
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第11回
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Learner Autonomy and AI – Personal Learning Environments / Concepts of learner autonomy and self-regulation; AI as a tool for planning, practicing, and reflecting on learning / Activities: - Create a learner profile (e.g., student aiming to raise TOEIC score by 100 points in 6 months); - Design an AI-supported self-study plan and weekly routine
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第12回
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Project Planning Workshop – From Ideas to Lesson Plans (Workshop) / Explanation of final project requirements: Learning context, objectives, activities, assessment, AI use, ethical considerations / Activities: - Decide lesson topic and target learners; - Clarify roles of the teacher, learners, and AI; - Draft an outline of the lesson plan and receive instructor feedback
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第13回
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Micro-teaching Session 1 / Selected students/groups conduct micro-teaching (10–15 minutes) based on their lesson plans; Classmates act as learners / Activities: - Micro-teaching; - Peer feedback in English using a structured form
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第14回
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Micro-teaching Session 2 & Synthesis / Remaining micro-teaching presentations; Identifying common patterns of success and challenges in AI-integrated lessons / Activities: - Group discussion: "What kind of English teacher do you want to be in the age of AI?"
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第15回
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Course Wrap-up and Future Directions / Review of key concepts and practices from the course; Discussion of future self-development and research possibilities (e.g., graduation thesis topics) / Activities: - Final Reflection (approx. 500 words): * How has your view of AI in ELT changed?; * What concrete ideas would you like to try in your future teaching?
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1. Class Participation and In-class Activities (30%)
- Active participation in English (questions, comments, discussions)
- Engagement in pair and group work
- Completion of in-class tasks and exercises
2. Short Assignments & Reflections (30%)
- Reflections (approx. 200–300 words, in English) at the end of each day
- AI interaction log (approx. 300 words) reporting how AI was used, what worked, and what problems appeared
3. Final Project: AI-integrated Lesson Design & Micro-teaching (40%)
- Written lesson plan (45–60 minutes) with clear learning objectives, assessment, and AI integration (approx. 1,000–1,500 words)
- Micro-teaching (10–15 minutes) based on part of the lesson plan
- Peer feedback in English on classmates' micro-teaching
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Before the course (overall):
Students are expected to spend approximately 2 hours on preparation before the intensive course begins. This includes:
- Setting up and briefly trying the generative AI tools to be used in class
- Preparing short English texts (e.g., self-introduction) for use in the first session
After the course (overall):
Students are expected to spend approximately 4 hours on follow-up work after the intensive course. This includes:
- Completing and polishing the final AI-integrated lesson plan
- Organizing and reflecting on their AI interaction log
- Writing the final reflection
During the intensive period (per day)
During the 4 days of the intensive course, students are expected to spend:
- About 1–1.5 hours per day on preparation (e.g., reading short texts for the next day, reviewing feedback, preparing prompts or ideas for in-class activities)
- About 1–1.5 hours per day on review (e.g., summarizing the day's content in English, writing the daily reflection of 200–300 words, organizing AI interaction records)
In total, the expected amount of out-of-class study time for this course is approximately 12–15 hours.
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本科目は、東京外国語大学・筑波大学・上智大学の大学院連携事業である「英語教育学イニシアティヴ・プログラム」(TEFL-IP)の一環として開講される集中講義です。東京外国語大学の大学院生で、英語教育学専攻ではない者が履修を希望する場合は、事前にTEFL-IP事務室 <tefl-ip@tufs.ac.jp> まで連絡して了承を得てください。【集中講義日程：1月27日～30日（オンライン）】
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This is an intensive course offered as part of the TEFL Initiative Program, a collaborative project run by the Graduate Schools of TUFS, University of Tsukuba, and Sophia University. If you are a TUFS graduate student who does not major in TEFL but who wishes to register for this course, please contact the TEFL-IP Office <tefl-ip@tufs.ac.jp> before registration. 【Class schedule: January 27-30 (online)】
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