Let’s break this down:

1. Gamified AI Design in EFL Listening

Gamification typically involves points, levels, badges, leaderboards, challenges, and narrative progression. In EFL listening contexts, this might look like:

• Completing “listening quests” with increasing difficulty.
• Earning stars for accurate comprehension or repeated listening.
• Unlocking new “worlds” (topics/accents) after mastering prior ones.

Impact on EFL Listening:→ Positive: Gamification leverages dopamine-driven feedback loops, which can increase time-on-task and persistence — crucial for listening, which requires repeated exposure. Studies (e.g., Sailer & Homner, 2020) show gamified environments improve attention and reduce perceived cognitive load.

→ Caveat: Over-gamification can distract from deep listening. If the “game” becomes the goal (e.g., collecting badges), learners may skim content or guess to progress — undermining authentic listening skill development.

Impact on Self-Regulation:→ Mixed: Gamification can scaffold self-regulation through goal-setting (e.g., “reach Level 5 by Friday”) and progress tracking. However, extrinsic rewards may crowd out intrinsic motivation (Deci & Ryan’s SDT), reducing learners’ ability to self-regulate without game mechanics.

Impact on AI Literacy:→ Limited: Most gamified systems hide the “AI behind the curtain.” Learners interact with the output (e.g., adaptive difficulty) but rarely understand how the AI adapts. Thus, AI literacy remains low unless explicitly taught.

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2. Conversational AI Design in EFL Listening

Conversational AI (e.g., chatbots, voice assistants like Replika, ELSA Speak, or ChatGPT-based tutors) simulates human dialogue. In EFL listening, this might involve:

• Real-time spoken interactions with feedback.
• Dialogic storytelling or Q&A after audio clips.
• Personalized conversational feedback (“You missed the intonation cue — let’s try again.”)

Impact on EFL Listening:→ High Potential: Conversational AI promotes active listening through reciprocity — learners must comprehend to respond. The social presence (even if simulated) triggers deeper cognitive processing (Media Equation Theory, Reeves & Nass). Also, adaptive dialogue can target individual listening gaps (e.g., connected speech, inference).

→ Risk: If the AI’s speech synthesis or NLP is flawed, it may reinforce miscomprehensions or frustrate learners.

Impact on Self-Regulation:→ Stronger Scaffold: Conversational AI can model metacognitive talk (“Why do you think the speaker said that?”) and prompt reflection — key for self-regulated learning (Zimmerman, 2002). It can ask: “What strategy did you use? What will you try next time?”

→ Caution: Over-reliance on conversational prompts may create “scaffold dependency” — learners wait for the AI to cue reflection rather than initiate it themselves.

Impact on AI Literacy:→ Higher Potential: Conversational interfaces naturally invite curiosity about how the AI “understands” or “responds.” With well-designed prompts (“I’m using speech recognition to assess your pronunciation — want to see how it works?”), learners can develop functional AI literacy — understanding capabilities, limits, and data use.

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3. Comparative Synthesis: A Learning Systems View

Think of both designs as dynamic systems:

• Gamified AI = closed-loop reward system → optimizes for engagement & persistence.
• Conversational AI = open-loop dialogic system → optimizes for reflection & adaptability.

In mathematical terms:

Engagement (Gamified) ∝ f(Rewards, Progress Visibility, Competition) Regulation & Literacy (Conversational) ∝ g(Dialogic Depth, Metacognitive Prompts, Transparency)

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4. Recommendations for Design & Research

• Hybrid Models Win: Combine gamified motivational structures with conversational metacognitive scaffolds. Example: Earn a badge not just for completing a listening task, but for explaining how you inferred meaning from context.
• AI Literacy Must Be Explicit: Embed “AI explainers” — mini-modules that show how speech recognition, NLP, or adaptive algorithms work. Treat AI literacy like numeracy: a core 21st-century competency.
• Measure Beyond Scores: Use learning analytics to track patterns of self-regulation (e.g., frequency of replaying audio, use of help features, reflection logs) — not just listening accuracy.
• Cultural & Individual Differences Matter: Not all learners respond to competition (gamification) or anthropomorphism (conversation). Personalization algorithms should adapt design mode based on learner profile.

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Final Thought (from a Math Ed Lens):

“The most effective AI for learning isn’t the one with the best algorithm — it’s the one that best models the learner’s thinking and feeling.”

In EFL listening, self-regulation, and AI literacy, we’re not just teaching English or tech — we’re teaching learners to become adaptive, reflective, and critical agents in complex systems. That’s a profoundly mathematical endeavor.

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Key References (for further reading):

• Deci, E. L., & Ryan, R. M. (2000). Self-determination theory.
• Sailer, M., & Homner, L. (2020). The gamification of learning.
• Reeves, B., & Nass, C. (1996). The Media Equation.
• Zimmerman, B. J. (2002). Becoming a self-regulated learner.
• Roll, I., & Wylie, R. (2016). AI in education: The illusion of learning.

Would love to hear how you’re designing or researching in this space — happy to dive deeper into metrics, system architectures, or pedagogical frameworks.

Sergio Leal Ramirez

In response to your insightful comparative discussion of gamified adaptive systems versus non‑gamified conversational agents, I would like to present key empirical findings from my own mixed‑methods study, AI‑Driven Gamified and Non‑Gamified Platforms in EFL Listening: Comparative Effects of Duolingo and Replika on Comprehension, Engagement, and Motivation (Tajik, 2025). This study directly examined both platforms under parallel classroom conditions with Iranian secondary school learners.

Gamified AI and Listening Gains

You noted that “over‑gamification can distract from deep listening” or encourage guessing. In my research, however, Duolingo’s integrated gamification and adaptive feedback produced balanced and statistically significant gains across all five measured listening subskills (F(4, 125) = 2.317, p = .078). The experimental group’s total score increased from M = 26.02 to M = 31.32, significantly surpassing the Replika group (M = 26.42, t(51) = 6.085, p 

Gamified adaptive systems are effective at increasing practice volume and facilitating behavioral regulation but may lead to undue reliance on extrinsic motivators and eventual dependence on outside forces to externalize limited AI literacy and practice. Non-gamified conversational agents can increase authentic listening, metacognitive regulation, and critical AI literacy but may lack the motivational scaffolding needed to be effective. Finding a middle ground may ultimately be a tipping point for not only language learning outcomes but whether adolescents leave school as passive consumers of AI technology or critical, reflective users.

Dear Colleague,

While your insight regarding potential over-reliance on extrinsic rewards in gamified AI is valuable, the empirical evidence presented in Tajik (2025) suggests a more nuanced mechanism: the Scaffolded Motivator Model (see Figure 1). In this model, gamification is embedded within an adaptive, theory-driven scaffolding process, positioning it not as a superficial engagement layer but as a dual-function pedagogical engine.

Evidence from the Study

• Quantitative gains: Duolingo group achieved a 27.8 % improvement in listening comprehension scores (F(1,51) = 18.72, p 
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1. Gamified AI Designs

Gamified designs embed points, levels, leaderboards, badges, and challenges into EFL (English as a Foreign Language) listening practice.

• Impact on Listening Skills Encourages repeated practice since learners are motivated to “win” or “level up.” Boosts attention span during listening tasks (students listen carefully to achieve rewards). Can sometimes prioritize speed over depth, so listening comprehension may become superficial if not well-balanced.
• Impact on Self-Regulation Strengthens behavioral self-regulation (students manage time and effort to reach goals). Short-term extrinsic motivation (playing for rewards) is strong, but long-term intrinsic motivation may suffer if the game elements are removed. Helps weaker learners persist when tasks feel hard.
• Impact on AI Literacy Students see AI mostly as a tool for entertainment and motivation. May lead to surface-level understanding of AI functions (e.g., “the app gives me points”), but not deeper literacy about how AI processes listening input.

2. Conversational AI Designs

Conversational AI (like chatbots, voice assistants, or AI tutors) focuses on dialogue-based interaction with learners.

• Impact on Listening Skills Provides authentic listening opportunities through natural spoken exchanges. Encourages learners to adapt to different accents, speech rates, and contexts. Strengthens active listening, since students must understand in order to respond.
• Impact on Self-Regulation Develops cognitive and metacognitive regulation (planning, monitoring, and evaluating responses). Fosters intrinsic motivation, as learners feel they are engaging in “real” communication. Requires higher discipline to sustain practice, since it’s less “playful” than gamification.
• Impact on AI Literacy Builds stronger awareness of how AI processes language (speech recognition, error correction, adaptive feedback). Learners begin to question biases, limitations, and reliability of conversational AI. Promotes critical human-AI collaboration skills, which are central to AI literacy.

3. Comparative Insights

AspectGamified AIConversational AIListeningImproves attention & practice repetitionImproves comprehension & adaptabilitySelf-RegulationBoosts short-term persistence, reward-drivenDevelops deeper reflection & autonomyAI LiteracySurface-level (AI = points system)Deeper-level (AI = partner, with strengths/limits)

4. Best Practice: Hybrid Approach

The strongest impact comes when gamification and conversational AI are blended:

• Gamification → sustains motivation and lowers anxiety.
• Conversational AI → develops authentic listening and higher-order regulation.
• Together → balance engagement + depth, while improving both language proficiency and AI literacy.

Dear Nazil,

Thank you for your valuable insights on gamification in language learning. Your expertise is highly appreciated.

If possible, I would be grateful if you could visit the link below and share your thoughts on my recent preprint article:

🌟 Recently Reached a New Theory – The Scaffolded Motivator Model

Our latest study didn’t just compare Duolingo’s gamified platform with Replika’s conversation-driven AI for teenage EFL learners — it led us to a new theoretical framework: the Scaffolded Motivator Model.

This model explains how adaptive gamification can sustain engagement, enhance listening comprehension, and foster self‑regulation, while conversational AI deepens reflective learning — and how blending them strategically can produce more autonomous, critically aware language learners.

📄 Read the full theory, data, and implications here:

https://doi.org/10.21203/rs.3.rs-7558517/v1

💬 Your feedback and scholarly debate are highly encouraged — they will help refine this model before its journal publication.

With appreciation,

Aliakbar Tajik