Game-Based Learning (RPGBL), Task-Based Language Teaching (TBLT), and generative artificial intelligence (GenAI) for English as a Foreign Language (EFL) instruction across diverse age demographics by Michael Armstrong

Game-Based Learning (RPGBL), Task-Based Language Teaching (TBLT), and generative artificial intelligence (GenAI) for English as a Foreign Language (EFL) instruction across diverse age demographics by Michael Armstrong

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  Revisiting RBGBL Curriculumin the age of AI Abstract This paper presents a seven-week instructional framework integrating Role-Playing Game-Based Learning (RPGBL), Task-Based Language Teaching (TBLT), and generative artificial intelligence (GenAI) for English as a Foreign Language (EFL) instruction across diverse age demographics (ages 8–24). The pedagogical design prioritizes face-to-face, off-screen communicative interaction, subordinating GenAI tools as episodic rendering apparatuses rather than primary instructional interfaces. By establishing a persistent avatar persona, the framework mitigates communicative anxiety (the affective filter), creates an ongoing semantic scaffold for cumulative lexical retrieval, and fosters learner self-efficacy through the generation of tangible multimodal artifacts. Revisiting RBGBL Curriculum in the age of AI It has been just over 10 years since I wrote my masters thesis and many of my theories have been proved over and over again, while other areas were found lacking. Technology has improved by leaps and bounds, and because of that, some of the core weaknesses in my curriculum design have been solved. The biggest one, being the ‘bottle-neck’. In my original model, the teacher functioned as the single "game engine," narrative designer, and feedback provider, a high-cognitive-load role that limited real-time interaction. In the GenAI-enhanced model, the AI acts as dynamic scaffolding and sandbox infrastructure, shifting the student's role from a passive prompt-user to an active narrative director, interlocutor, and critical editor. Generative AI simply removes the operational bottleneck that previously made Role-Playing Game-Based Learning (RPGBL) difficult to scale in a standard EFL classroom. What has proved true from my thesis in 2016 is that CMC and other screen-based learning paradigms have fallen short. Treating AI as a vending machine rather than a running machine for the mind has created an illusion of competence among learners of all ages. 'Screen based' learning has drawbacks that are not being addressed by education systems. Gen-Z is the first generation to actually score lower than previous generations in academics. AI can help mitigate that loss, but not if it is used as an all-in-one replacement. There are clear benefits to AI and clear short-falls. As a curriculum designer that regularly uses AI in the classroom, I can tell you that one of the best benefits is in motivation. 3rd space learning is enhanced.
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  Using it toremove the drudgery around learning...but if that is all, and there is no foundation behind work output, it is baseless.If AI is used solely to generate work without an underlying grasp of the material, the output is hollow. Lasting academic growth still depends entirely on students building a strong conceptual foundation behind the tools they use. My answer? Scaffolding, shifting rubrics and meta-cognitive training. Scaffolding: Students created hypothesis, AI assisted research, manual corrections, in class debates Shifting grading rubrics: stop grading output, make grading the entire process and 'live comprehension' have actual point value. Meta-cognitive skill training: (the most important leg of the tripod) Teach prompt literacy, source verification, and bias detection so students understand the machinery behind the outputs rather than treating AI as an infalible hybrid of Thoth and Hermes. Old model (2016 RPGBL) Student creates character ↓ Student creates narrative ↓ Teacher creates scenarios ↓ Students role-play ↓ Teacher provides feedback ↓ Students continue the story ____________________________________________ GenAI-enhanced model (2026) Student creates character ↓ AI helps visualize the character ↓ AI becomes an interactive NPC ↓ AI generates/adapts scenarios ↓ Students communicate with characters in English ↓
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  AI provides linguisticfeedback ↓ Students critique AI's responses ↓ Students revise the narrative ↓ Students create music/images/video/world-building artifacts ↓ Students reflect critically on what AI produced That is a very different educational paradigm from "use ChatGPT to write your essay." And my earlier work seems compatible with it. My thesis specifically emphasizes student ownership of avatars and narratives and using fictional environments to motivate language acquisition. The following is an analytic breakdown of a class project entitled ‘Ninja Grandmother”, this follows the adaptation of my 2016, RPGBL Curriculum, As an example for we can look to “Ninja Grandmother” , a successful application of my Gen AI -RPGBL curriculum with a class of 12 Korean elementary school children. Theoretical Framework The curriculum design rests on three core pedagogical and Second Language Acquisition (SLA) principles from my previous RPGBL curriculum which can be found here… https://hdl.handle.net/20.500.14755/2046 (8/21/2026) or https://repository.stcloudstate.edu/items/0290275b-61dc-43b0-8dfe-2777d5c10fa7 (8/21/2026) Affective Filter Reduction via Persona Adoption: Shifting the locus of discourse from first-person self-disclosure to avatar-mediated communication provides psychological distancing. Learners engage in target language production behind a "protective persona," diminishing social vulnerability and speaking anxiety while supporting self-efficacy. Cumulative Memory Architecture & Spaced Retrieval: Rather than isolating weekly thematic vocabulary, the character creation process serves as a continuous semantic anchor. Lexical items introduced during early descriptive stages are systematically recycled and elaborated across subsequent biographical and narrative phases.
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  Episodic AI Checkpointsin Task-Based Language Teaching (TBLT): GenAI models (diffusion-based image synthesizers, large language models, and neural audio generators) serve strictly as external rendering engines. Meaning negotiation, peer collaboration, and lexical selection remain off-screen, with AI functioning as an immediate semantic verification tool. Methodological Sequence: Seven-Week Curriculum Design Phase 1: Physical Visualization & Lexical Constraint (Weeks 1–2) ●​ Target Competence: Physical anatomy, descriptive adjective ordering, apparel lexicon, and prompt constraint formatting. ●​ Classroom Procedure: Learners collaborate off-screen to define an original protagonist or antagonist. Descriptive output is governed by strict lexical constraints requiring two target adjectives per anatomical and clothing feature (e.g., hair, eyes, facial features, torso, limbs, garments, and carried artifacts). ●​ AI Integration: Lexical profiles are submitted to a free, filtered image generator (e.g., Perchance, configured with safety parameters). The resulting visual output provides immediate, non-judgmental semantic feedback regarding the descriptive accuracy of the student's adjectives. ●​ Phase 2: Identity Crystallization & Affective Distancing (Weeks 2–3) ●​ Target Competence: Past and present simple tense, personality adjectives, biographical connectors, and motivational discourse. ●​ Classroom Procedure: Students assign three dominant personality traits and complete an in-depth biographical inventory in-character. The questionnaire addresses foundational demographics (age, birthplace, family structure), ethical/behavioral inclinations (crime, secrets, gut vs. head decision-making, altruism vs. self-interest), and core psychological drivers (general/specific motivations, fears, preferences). ●​ Pedagogical Function: This biographical battery anchors the persona, enabling spontaneous communicative exchanges where learners defend decisions from their avatar's perspective. Phase 3: Ability Mechanics & Constraint Balancing (Week 4) ●​ Target Competence: Modals of ability (can/cannot, capable of), comparative scales, and balanced system design. ●​ Classroom Procedure: Learners delineate their character's operational capabilities
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  using a structuredgame-balance constraint: ​ ○​ Three human-tier physical or technical skills. ○​ ○​ Two animal-tier sensory or environmental adaptations. ○​ ○​ One supernatural power (with temporal manipulation and teleportation restricted to preserve narrative tension). ○​ Phase 4: Multimodal Synthesis & Artifact Production (Weeks 5–6) ●​ Target Competence: Semantic distillation, summarization, thematic vocabulary selection, and poetic meter. ●​ Classroom Procedure: Students synthesize their accumulated dossier into a structured, ten-point factual summary. ●​ AI Integration: The ten core points are input into a Large Language Model (e.g., Gemini) with explicit prompts to structure metric song lyrics based strictly on student-generated lore. The finalized text is converted into an audio track using a neural music synthesis platform (e.g., Suno). Phase 5: Metalinguistic Genre Transformation & Critical Analysis (Week 7) ●​ Target Competence: Register variation, comparative mood adjectives, instrumentation lexicon, and evaluative critique. ●​ Classroom Procedure: The class analyzes how transforming musical genres (e.g., hip-hop, traditional folk, orchestral) alters the rhetorical tone and narrative impact of their English lyrics. Student tracks are compiled, compared, and collectively evaluated alongside character image portfolios (e.g., the Ninja Grandmother multimodal case study).
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  Summary Curriculum Matrix(7 week plan) Phase / Timeline Core SLA Objective Off-Screen Interaction GenAI Deployment (Rendering Engine) Multimodal Artifact Weeks 1–2 Adjective order, anatomy, clothing Collaborative physical character design Text-to-Image Diffusion (Perchance) Visual character portrait Weeks 2–3 Personality traits, biographical discourse In-character questionnaire completion & peer interviews Structured Prompt Consolidation Comprehensive Character Dossier Week 4 Modal verbs of ability, game mechanics Skill balancing & constraint negotiation System Rule Verification Standardized Stat/Ability Sheet Weeks 5–6 Summarization, poetic meter, rhythm 10-point semantic distillation & lyric drafting LLM (Gemini) + Neural Audio (Suno) Fully arranged musical track Week 7 Metalinguistic analysis, register shifts Genre comparison & peer presentation Multi-genre audio iteration Final multimedia showcase video
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  Pedagogical Implications This modeldemonstrates that generative AI can be integrated into language curricula without converting classrooms into passive digital environments. By maintaining an off-screen pedagogical core, learners retain agency over narrative conceptualization and critical evaluation. The resulting multimodal artifacts provide tangible evidence of linguistic competence, driving significant gains in learner self-efficacy and long-term communicative retention. Song example… The entire class created this song and it is indicative of the class’ narrative conceptualization for their class-made character ‘Ninja Grandmother’ https://suno.com/song/d165b979-3e21-4c21-9780-32b8febf5dbc (8/21/2026) Ninja Grandmother In the crags of Mungyeong, where the mist meets the pine I’ve walked the earth since the ancient line Three hundred and ninety years of the sun But this old woman’s work is never done I move like a ghost, I breathe with the trees Bringing the invaders to their trembling knees. [Chorus] I am Ninja Grandma, the shadow and the light A Korean legend in the dead of the night In a black Hanbok and a silken veil I’m the iron heart in a body frail With a sword of fire and a warrior’s soul I keep the spirit of my nation whole. [Verse 2] My stealth is a whisper, survival my song I’ve stayed in the darkness for far too long When the Japanese banners rise over the hill I sharpen my hunger, I sharpen my will For the ones who crossed the Eastern Sea There is no mercy in the hands of me. [Bridge] I’ve mastered the kitchen, I’ve mastered the blade I’ll nag you for hours in the cedar shade My cooking is salt, my charm is a trap I’ll hold the whole world in my wrinkled lap Exper teez in the hearth and the heat of the fray I’ll feed you a feast then I’ll sweep you away. [Chorus] I am Ninja Grandma, the shadow and the light A Korean legend in the dead of the night
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  In a blackHanbok and a silken veil I’m the iron heart in a body frail With a sword of fire and a warrior’s soul I keep the spirit of my nation whole. [Outro] Four centuries of secrets, a lifetime of war I’m the grandmother guarding the mountain door Put on your coat, the night is getting cold... But the fire I carry is ten-thousand years old. Evidence of student retention and while some of the lyrics may appear violent, one has to consider the Korean context and how passionate some children are about their countries' not too distant past. ●​ Direct Traceability of Curriculum Steps: ○​ Weeks 1–3 (Physical & Cultural Bio): Details like "390 years of the sun," "in the crags of Mungyeong," "a black hanbok and a silken veil," and "body frail" directly reflect the structured questionnaire and physical adjective prompts. ○​ Week 4 (Skills & Supernatural Powers): The lyrical references to moving like a ghost, mastering the kitchen/blade, and wielding a "sword of fire" align with the 3 human / 2 animal / 1 supernatural ability constraint. ○​ Weeks 5–7 (Artifact Generation & Genre Transformation): Condensing the character's profile into an energetic hip-hop track anchored by the chorus "Ninja Grandmother" turns abstract vocabulary into a memorable, high-tempo production.
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  Core Pedagogical Analysis Thiscurriculum succeeds because it treats generative AI as a high-fidelity rendering engine for student-negotiated language, rather than an automated surrogate for student cognition. By keeping the discursive, creative, and evaluative work off-screen, the lesson plan resolves the common failure mode of EdTech implementations where screen time dilutes communicative language practice. Key Analytical Pillars 1. The "Protective Persona" & Affective Filter Reduction ●​ Mechanism: Shifting from first-person self-disclosure ("Tell me about your family") to avatar-mediated production ("Who is in your character's family?") provides crucial psychological distancing. ●​ Impact: For young learners (ages 8–10) and language-anxious students, language mistakes and emotional vulnerability belong to the fictional persona rather than the child. This dramatically lowers Krashen’s affective filter and encourages risk-taking in English production. 2. Linguistic Constraint as Prompt Engineering ●​ Mechanism: Requiring two adjectives per anatomical feature and clothing item transforms prompt formulation into a targeted lexical task. ●​ Impact: Visual outputs serve as instant, non-judgmental semantic feedback. If a student writes "black hanbok" or "wrinkled face," the generated visual immediately confirms whether the meaning was successfully conveyed. 3. Cumulative Memory Architecture & Spaced Retrieval ●​ Mechanism: Rather than discarding thematic units week-to-week (e.g., body parts in Week 1, hobbies in Week 2), the avatar acts as an ongoing semantic anchor. ●​ Impact: Physical descriptions in Week 1 feed the biography in Weeks 2–3, which dictates the powers in Week 4, and culminates in the lyrical synthesis of Weeks 5–7. Vocabulary undergoes spaced retrieval, elaboration, and contextual recycling over nearly two months. 4. Tangible Artifacts and Self-Efficacy ●​ Mechanism: Synthesizing the final song and cinematic visual gallery transforms abstract grammar drills into a polished cultural product. ●​ Impact: Students experience a tangible "mastery experience." Being able to point to an original hip-hop track ("Ninja Grandmother") and say, "I made that in English," builds intrinsic motivation and perceived linguistic competence. Structural Strengths vs. Implementation Considerations Pedagogical Dimension Key Strength Considerations for Scale Cognitive Load Clear scaffolding from micro-level words to Requires strict teacher oversight during the LLM
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  macro-level songs. summarizationstep to ensure students write the 10 facts themselves. Classroom Ecology Preserves face-to-face interaction; screen access is brief and task-specific. Dependent on stable, free-tier generator access (e.g., Perchance, Suno) with consistent SFW filters. Cultural & Linguistic Voice Encourages localized storytelling (e.g., Korean folklore, hanbok, Mungyeong). Needs balanced guidance so students don't rely on L1 for complex conceptualization without scaffolding back to L2. Mapping Curriculum Mechanics to Core Principles 1. Scaffolded Integration over Total Automation ●​ The "Sandwich" Model in Action: ○​ Top Layer (Manual Inception): Weeks 1–4 require students to brainstorm, debate, and write out physical traits, deep psychological dossiers, and skill sets entirely off-screen. ○​ Middle Layer (AI Iteration & Rendering): In Weeks 5–6, AI is introduced strictly as a rendering engine to visualize the character or convert 10 student-written factual propositions into rhythmic verse. ○​ Bottom Layer (Human Defense & Adaptation): In Week 7, students critique the generated songs, experiment across musical genres, and mash styles together, analyzing how tone and meaning shift. ●​ Focus on Process Over Artifact: The final "Ninja Grandmother" video is not graded as an isolated prompt output. It serves as evidence of cumulative, iterative language production over nearly two months of scaffolding. 2. Friction-Calibrated Learning ●​ Preserving Productive Struggle: Foundational vocabulary acquisition (anatomy, adjectives, past-tense biographical narration, modal verbs) is kept low-tech and conversational. The student must do the heavy cognitive lifting of inventing the character before touching a digital tool. ●​ Linguistic Constraint vs. Automation: The rule requiring "two adjectives per body part and clothing item" introduces calibrated friction into the prompt engineering process. The AI is not asked to "invent a cool character"; it is forced to render the exact syntactic
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  constraints generated bythe student. 3. Curriculum Redesign for "Base-First" Competency ●​ Prerequisite Mastery Gates: Students are not handed Suno or Gemini on Day 1. Access to the music and lyric accelerators in Weeks 5–7 is strictly gated behind completing the foundational character dossier (Weeks 1–4). Without the 10 synthesized biographical facts, the prompt cannot be constructed. ●​ Metacognitive Skill Training & Verification: In Week 1, visual generation acts as instant semantic verification. If a student writes "black hanbok" or "wrinkled hands," the AI image confirms whether their vocabulary was accurate. In Week 7, evaluating genre transformations trains students to critically analyze how AI tools handle emotional register and musical pacing. Alignment Matrix Framework Principle 7-Week Curriculum Implementation The "Sandwich" Model Human character ideation AI lyric/music rendering Human → → genre critique and style remixing. Productive Struggle Face-to-face peer interviews answering 15+ in-depth psychological questions before using digital tools. Process Over Artifact The final song is evaluated as the culmination of spaced lexical retrieval across a 7-week pipeline. Mastery Gating Lyric generation is unlocked only after completing physical traits, biography, and skill-balancing tiers. Metacognitive Training Evaluating whether the generated song matches the character's backstory and analyzing genre tone shifts.
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  Final Thoughts The Role-PlayingGame-Based Learning (RPGBL) GenAI pipeline succeeds because it subordinates artificial intelligence to human-centered, task-based language acquisition rather than allowing technology to displace face-to-face communication. Drawing directly on the theoretical foundations of my original 2016 RPGBL thesis, the framework leverages student ownership of avatars and immersion in fictional environments to naturally motivate language acquisition and enhance self-efficacy. Operating within a structured "sandwich" model, students perform the core cognitive, linguistic, and narrative work off-screen through peer negotiation, structured questionnaires, and strict lexical constraints. Generative AI tools are deployed only episodically as external rendering engines to materialize character visuals, dynamic scenario responses, and synthesized musical tracks from student-authored data. This cumulative memory architecture reinforces vocabulary through spaced retrieval across multi-week thematic units, transforming abstract grammar into tangible mastery artifacts. The resulting multimodal deliverables provide concrete evidence of linguistic competence, driving sustained learner motivation while training students to critically analyze, edit, and evaluate AI-generated outputs. **** Alternate 14 week curriculum*** Alternate lesson plan based on a 14 week semester. To structure this into an updated master’s thesis chapter or published syllabus, the curriculum is organized below as a 14-Week Task-Based Language Teaching (TBLT) Unit. Curriculum Structure: The RPGBL GenAI Pipeline Week Phase / Focus Target Linguistic Competence (CEFR A2–B1) Off-Screen Pedagogical Task GenAI Touchpoint / Artifact 1–2 Physicality & Form Adjective order, anatomy, clothing & gear lexicon Pair interviews sketching character outlines using descriptive Image generation via text prompts; visual verification of descriptive accuracy
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  vocabulary 3–4 Identity & Backstory Pastsimple, biographical connectors, expressing motivation & fear Answering deep-dive character questionnaires in-character; peer role-play Assembling a formalized digital Character Dossier 5 Abilities & Constraints Modals of ability (can/could/is able to), comparative scales Formulating balanced skill sets (3 human, 2 animal, 1 supernatural) Validating character constraints and skill balance 6–7 Thematic Synthesis Summarization, poetic cadence, core thematic vocabulary Distilling dossier into a 10-point highlight sheet; drafting verse rhymes Generating full lyrics and generating music via audio models 8 Genre & Metalinguistics Mood/genre adjectives, comparative analysis, rhythm terms Analyzing lyrical register; comparing how genre changes narrative tone Remastering/mashing tracks across different musical styles 9–10 The Interactive Encounter Question formation, conditional reasoning, pragmatic Verbal group problem-solvin g; debating quest strategy AI acts as interactive NPC/Dungeon Master, adapting to player choices
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  negotiation 11–12 Quest Resolution Narrative past tense, sequencing adverbs, dialogue mechanics Groupdrafting of story scenes resolving encounters Generating scene backgrounds or comic panels illustrating outcomes 13 Critical Evaluation Evaluative language, error correction, register analysis Metacognitive review: Did the AI maintain character voice? Were prompts optimal? Text review comparing initial student prompts vs. AI generations 14 Artifact Presentation Public speaking, presentation discourse, reflective commentary Live showcase of character tracks, visual dossiers, and story logs Final compilation of student-created multimedia portfolio