Hi! If you are coming here without context, you might not understand what is below. This writing is the results of a ChatGPT4.5 deep research query I did to show an example of what deep research can produce (within 6 minutes). I have put this writing here as an example for another post in which I was writing about using AI tools to do academic research. So, all the writing after this sentence is directly copied from my interaction with ChatGPT.

My initial prompt: I want to write a literature review about how conversational AI applications have been used to teach foreign languages (especially English).

ChatGPT said:

Here’s a structured outline and key points you might consider including in your literature review about conversational AI applications for foreign language teaching, specifically focusing on English:

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Suggested Literature Review Structure:

1. Introduction

• Briefly introduce conversational AI, emphasizing its recent emergence in language education.
• Clarify your scope: applications specifically focused on teaching English as a foreign language (EFL).

2. Theoretical Background

• Define conversational AI (chatbots, voice-based virtual assistants).
• Discuss underlying theories: sociocultural theory (Vygotsky), interaction hypothesis (Long), and communicative competence (Hymes).
• Explain the relevance of conversation-based learning in language acquisition.

3. Overview of Conversational AI Technologies in Language Education

• Chatbots (text-based, e.g., ChatGPT-based tutoring).
• Voice-based conversational agents (e.g., Alexa, Siri).
• Dialogue-based Intelligent Tutoring Systems (ITS).

4. Pedagogical Applications

• Practicing speaking skills and pronunciation.
• Developing communicative competence through authentic conversation.
• Supporting vocabulary acquisition and grammar practice within context.
• Providing personalized feedback and scaffolding learner autonomy.

5. Empirical Studies and Findings

Review relevant empirical studies, summarizing:

• Effectiveness in improving oral fluency and confidence.
• Learner engagement and attitudes toward using conversational AI.
• Limitations identified (e.g., accuracy issues, limited depth of conversation, learner frustration, privacy concerns).

6. Limitations and Challenges

• Technological limitations (accuracy, misunderstanding, unnatural interactions).
• Pedagogical limitations (lack of nuanced cultural awareness or deeper cognitive engagement).
• Ethical considerations (privacy concerns, data usage).

6. Emerging Trends and Future Directions

• AI-driven adaptive conversation practice.
• Multimodal interactions (voice, visual elements, virtual environments).
• Integration with augmented/virtual reality.
• Ethical, responsible AI usage in education.

7. Conclusion

• Synthesize key insights from existing research.
• Highlight gaps in the literature, suggesting areas for future research.

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Let me know if you’d like to explore a specific section or if you need help gathering relevant sources!

You said:

Thank you! Yes, can we focus on the “Technological limitations (accuracy, misunderstanding, unnatural interactions).” Can you write a research review related to that?

ChatGPT said:

Great! To focus on the technological limitations of conversational AI in foreign language learning, I’d like to clarify a few points:

1. Scope of AI Applications: Are you primarily interested in chatbots, voice assistants, or both?
2. Specific Aspects of Limitations: Do you want to cover accuracy in grammar/correction, speech recognition errors, contextual misunderstandings, or all of these?
3. Preferred Sources: Should the review focus on peer-reviewed academic studies, industry reports, or both?
4. Timeframe: Do you want research from the last 5 years, or would you like a broader historical perspective?

Once I have this information, I’ll start the research for you!

You said:

1. Both 2. All of these. 3. peer-reviewed academic studies 4. the past 5 years is great. AI has mainly become very good in the past 5 years. Thank you!

ChatGPT said:

Great! I will review peer-reviewed academic studies from the past five years on the technological limitations of conversational AI in foreign language learning. This will include issues related to accuracy in grammar correction, speech recognition errors, and contextual misunderstandings in both chatbots and voice assistants.

I will let you know once I have gathered the relevant research.Research completed in 4m · 17 sources

Technological Limitations of Conversational AI in English Language Learning (2018–2023)

Introduction

Conversational artificial intelligence (AI) is increasingly used as a language learning tool, offering chatbots and voice assistants as virtual conversation partners for English learners. These systems promise on-demand practice in speaking, listening, and even writing, but recent studies highlight significant technological limitations that affect their effectiveness. Researchers in applied linguistics and educational technology have examined how well such AI tools correct grammar, recognize and respond to speech, maintain natural dialogue, and engage learners. This review synthesizes key findings from peer-reviewed studies in the past five years, focusing on: (1) accuracy of AI grammar correction and feedback, (2) speech recognition errors in pronunciation and comprehension, (3) contextual misunderstandings and unnatural interactions, (4) impacts of these limitations on learner engagement and outcomes, and (5) emerging solutions to address these challenges. The goal is to summarize current knowledge on these issues and discuss implications for using conversational AI in language education.

Accuracy of Grammar Correction and Language Feedback

A common expectation is that AI conversation partners will provide corrective feedback on learners’ mistakes. In practice, many conversational AI applications struggle to deliver accurate, timely grammar correction or useful language feedback. A 2022 systematic review by Huang, Hew, and Fryer noted “technological limitations” as a major challenge for chatbot-based language learning​ eric.ed.gov – often referring to the AI’s inability to notice or correct learner errors effectively. Koc and Savas’s 2024 meta-synthesis of voice-based chatbots similarly found a “lack of explicit corrective feedback” was among the most frequently reported problems in studies (reported in 5 out of 24 studies reviewed)​ cambridge.org. In other words, chatbots often fail to correct grammar or provide explanations when a learner makes a mistake, limiting their usefulness as language tutors.

Recent experiments have tried to improve how chatbots handle corrective feedback. Liang et al. (2023) examined different feedback delivery methods in an AI chatbot for English grammar practice aclanthology.org . They found that most current chatbots lack synchronous (immediate) error correction and do not support learner self-correction, even though research suggests immediate feedback and prompting learners to self-correct can enhance learning​ aclanthology.org. In their study, the researchers implemented a conversational corrective feedback (CCF) system that offered instant grammar hints and explanations during a chat. Notably, their results showed learners benefited more when the chatbot prompted them to correct their own errors (guided self-correction) rather than the bot simply giving the right answer aclanthology.org. They also discovered that delivering corrections through a graphical user interface (e.g. a pop-up or sidebar) was more effective than inserting corrections directly into the chat dialogue​ aclanthology.org. These findings suggest that improving feedback design – for example, using the interface to highlight mistakes and giving users a chance to try again – can make AI grammar correction more accurate and pedagogically helpful. Overall, however, fully reliable grammar feedback remains an unsolved challenge: most conversational AI tools today still miss many learner errors or correct them inappropriately, providing far less nuanced feedback than a human teacher or tutor would.

Speech Recognition Errors and Pronunciation Issues

For voice-based AI conversation practice, speech recognition accuracy is critical – yet studies show that automated speech recognition (ASR) often falters with language learners. Recognition errors can cause the AI to misunderstand or ignore the learner’s speech, undermining the interaction. In a case study using Amazon’s Alexa voice assistant with English learners, Dizon (2017) reported that Alexa could only comprehend roughly half of the learners’ spoken utterances files.eric.ed.gov . The other half were not understood due to mispronunciations or non-standard accents, highlighting the limitations of the technology in handling L2 (second-language) speech files.eric.ed.gov. Learners in that study noted that when Alexa failed to understand them, it essentially served as implicit feedback on their pronunciation – alerting them that their speech was unclear​ files.eric.ed.gov. However, from a learning perspective, frequent misunderstandings can be frustrating. Kukulska-Hulme and Lee (2020), after reviewing a decade of research on intelligent assistants in language learning, concluded that these tools “can also fail to comprehend meaning or accented pronunciation” when learners speak​ files.eric.ed.gov. In other words, strong accents, mispronounced words, or even minor grammar mistakes can confuse the AI speech recognizer, leading to breakdowns in the conversation.

A comprehensive meta-synthesis by Koc and Savas (2024) found speech recognition problems to be the most commonly reported technical challenge in AI-driven language learning. Out of the studies they analyzed, 15 reported issues with “low intelligibility of learner utterances” because the ASR system struggled to recognize learners’ speech​ cambridge.org . These errors directly affect pronunciation practice and listening comprehension: if the AI doesn’t catch what was said, it cannot respond meaningfully, and the learner gets no useful feedback. Learners may become hesitant to speak or repeat themselves unnaturally when the system repeatedly says “I’m sorry, I didn’t get that.” Over time, this can erode the perceived benefit of speaking with the AI. Some research even indicates that frustration with recognition errors can diminish learners’ motivation to continue using the tool​ cambridge.org. On the positive side, a few studies have noted that interacting with a non-judgmental AI can lower speaking anxiety for some learners, since machines don’t mind if you make mistakes. For instance, one experiment found that learners’ anxiety was lower in conversations with an AI partner compared to a human partner, possibly because the AI was seen as a patient listener, helping reduce pressure cambridge.org. But this benefit is only realized if the AI can reliably understand the student’s speech. When recognition is spotty, any anxiety reduction may be offset by frustration or embarrassment when the AI fails to respond correctly.

Researchers have begun exploring solutions to improve ASR performance for language learners. One recommendation is to train speech recognition models on non-native English data cambridge.org . Many commercial voice AIs (like Siri, Alexa, or Google Assistant) are trained primarily on native speaker speech, which means they perform poorly for accented or learner speech. By incorporating diverse L2 English pronunciations in training data, the AI can better “learn” to decode non-native utterances​ cambridge.org. Another avenue is customizing the AI to a particular learner’s voice over time (speaker adaptation), though this is still an emerging area. In summary, inaccurate speech recognition remains a major technical limitation of conversational AI in language learning – often leading to miscommunication and missed learning opportunities in pronunciation practice.

Contextual Misunderstandings and Unnatural Interactions

Even when the AI catches the words a learner says, true conversational understanding is limited. Several studies point out that AI dialogue systems often respond in ways that feel out-of-context or unnatural, which can disrupt the flow of conversation. The root issue is that current conversational AIs (especially those used in learning apps until recently) have only shallow dialogue capabilities. They may follow scripted patterns or use basic AI that cannot handle the unexpected ways learners might phrase questions or switch topics. Kukulska-Hulme & Lee (2020) observed that AI assistants sometimes simply fail to comprehend the meaning of what learners say​ files.eric.ed.gov. Such misunderstandings mean the AI’s reply might be irrelevant or nonsensical from the learner’s perspective. For example, a learner’s open-ended question or a slight change of topic could confuse a rule-based chatbot, leading it to give a generic or incorrect response. These contextual failures make the interaction feel stilted and “robotic.” Learners might get the sense that the chatbot is ignoring what they actually meant, which reduces the authenticity of the conversation.

In fact, maintaining a natural, context-aware dialogue has proven so difficult that some experts have favored very constrained chatbot designs for language practice. Godwin-Jones (2022) notes a consensus in CALL research that “rule-based, scripted” chatbots – with predictable, controlled interactions – were long considered optimal for learning eric.ed.gov . By restricting topics and using fixed prompts, these systems avoid random misfires and keep conversations on track. The downside, however, is that such scripted interactions can become too predictable and inauthentic. Learners may quickly realize the chatbot is just following a script, which can limit engagement and spontaneous language use. On the other hand, fully “open” AI chatbots (like general AI assistants) historically lacked focus: they often could not provide task-oriented or context-specific dialogue that learners need​ eric.ed.gov. This creates a trade-off between controlled interactions (fewer bizarre responses but less spontaneity) and open-ended interactions (more natural potential, but higher chance of confusion). Until recently, the technology struggled to achieve both accuracy and conversational richness.

Multiple empirical studies document the “unnaturalness” of AI-driven conversations as a genuine problem. In the 2024 meta-analysis, 9 studies out of the sample reported that interactions with AI chatbots felt unnatural or awkward cambridge.org . Learners often commented that the dialogue did not flow like a human conversation – the chatbot might repeat itself, ignore certain questions, or give tangential answers. Importantly, this lack of natural interaction was tied to engagement: several studies found that learners grew less motivated to chat with the AI over time, especially when the novelty wore off cambridge.org. Fryer et al. (2017) observed in a longitudinal experiment that students’ interest in practicing with a chatbot dropped as sessions went on, preferring human partners when available cambridge.org. Participants cited the formulaic or mechanical communication style as one reason the chatbot became less stimulating​ cambridge.org. Additionally, chatbots today have little capacity for emotional or cultural context. They lack empathy and nuanced understanding of social cues. Cakmak (2022) and El Shazly (2021) both reported that some learners actually experienced increased anxiety or self-consciousness when interacting with AI, partly because the chatbot couldn’t truly connect with them or respond supportively to communication breakdowns​ cambridge.org. The absence of human-like empathy and the inability to handle complex or subtle inputs are clear symptoms of how contextually limited these AI systems remain.

In summary, current conversational AI often fails to deliver a smooth, context-aware dialogue. Whether due to limited dialogue management (in simpler chatbots) or the quirks of advanced models that still misinterpret intent or give unnatural responses, the result is interactions that can feel artificial. This undermines the potential benefits of practicing language in a free-form conversation. True conversation is dynamic and context-sensitive; today’s AI is only beginning to approach that level of sophistication.

Impact on Learner Engagement and Outcomes

The limitations outlined above have direct consequences for learners’ engagement, motivation, and language gains. When an AI tutor frequently mishears the learner, misunderstands their intent, or fails to provide useful feedback, learners may become frustrated or disengaged. Research indicates that initial enthusiasm for a conversational agent can taper off if the experience is riddled with errors or unnatural exchanges. In a 2019 study on chatbot learning partners, Fryer and colleagues found that while students were curious and excited at first, their sustained interest depended on the chatbot’s ability to hold a meaningful conversation cambridge.org . Many learners lost interest when interactions felt repetitive or confusing, preferring a human interlocutor who could adapt to their needs​ cambridge.org. This is echoed by Belda-Medina & Calvo-Ferrer (2022), who reported only moderate continued use of an AI chatbot for language practice – interest dropped after the novelty effect dissipated​ cambridge.org.

Another critical issue is learner confidence and anxiety. Ideally, practicing with an AI should be a low-stakes opportunity that builds confidence in using English. Some studies do report positive effects, such as reduced speaking anxiety when talking to a non-judgmental machine. For example, in controlled comparisons, students sometimes feel more relaxed making mistakes with a chatbot than with a peer or teacher, which can encourage them to speak more freely​ cambridge.org . However, this benefit can be negated if the AI’s limitations cause new forms of anxiety. Communication breakdowns – constantly having to repeat oneself or getting nonsensical replies – can make learners doubt their own abilities (“Am I pronouncing this so poorly?”) or the value of the practice. Çakmak (2022) observed heightened anxiety in students after using an AI chatbot, attributing it to factors like the academic context (fear of failing a task) combined with the chatbot’s lack of human empathy cambridge.org. In that scenario, the bot’s occasional corrections or robotic responses may have made students more self-conscious without the reassuring cues a human tutor might provide.

As for learning outcomes, the evidence is still emerging. Some studies in the review period have measured gains in speaking or vocabulary after using AI chat systems, often finding modest improvements when the technology functions well. The consensus, though, is that AI partners are not yet a replacement for rich human interaction in language learning. They can provide extra practice, but if learners disengage due to the AI’s quirks, the quantity and quality of practice suffers. The lack of nuanced feedback (e.g. not correcting grammar or pragmatics) also means learners might reinforce errors or develop a skewed sense of real-world communication if the AI lets unnatural patterns slide. In essence, the current limitations can diminish the educational value of these tools: an unreliable conversational partner may offer less language input and feedback, leading to fewer gains than expected. This underlines why many researchers advise using AI conversational tools as a supplement – not a standalone solution – and closely monitoring learner engagement and progress.

Emerging Solutions and Advancements

Despite the challenges, recent years have seen active research and development aimed at overcoming these limitations. Both incremental improvements and leap-ahead technologies are being explored to make conversational AI more effective for language learners:

• Better Speech Recognition & Pronunciation Support: To tackle ASR issues, developers are training models on diverse non-native speech data and improving accent recognition​cambridge.org. An important recommendation is to include learner pronunciations in AI training sets, so the system becomes more robust to varied accents and errors​cambridge.org. Some systems now also provide feedback on pronunciation by analyzing the learner’s speech (for example, highlighting mispronounced words), though accuracy of detailed pronunciation scoring is still improving.
• Enhanced Natural Language Understanding: Advances in AI language models promise more context-aware dialogue. Whereas older chatbots had limited memory and flexibility, the latest large language models (LLMs) like Google’s LaMDA and OpenAI’s GPT-4 are far more capable of sustaining open-ended conversations. These models can draw on vast context windows and have been trained on huge amounts of human dialogue. Researchers suggest that integrating such LLMs into language learning chatbots could enable more natural, wide-ranging discussions​cambridge.org. As Godwin-Jones (2022) argued, extending chatbots with large language models “has the potential to combine the advantages of language practice through open-ended conversation” with better responsiveness​eric.ed.gov​cambridge.org. Early trials indicate that LLM-based chatbots can indeed stay on topic better and respond more coherently than earlier generations, though careful tuning is needed to maintain pedagogical usefulness (e.g. avoiding inappropriate content and ensuring the level of language is appropriate for the learner).
• Improved Conversational Design: Researchers are refining how chatbots deliver feedback and interact. One approach is to add a “meta-layer” of feedback outside the chat – for instance, the chatbot interface can display hints, translations, or corrections unobtrusively while the conversation continues. This was the strategy in the ChatBack study, which found that a GUI-based feedback display led to more effective grammar correction than the chatbot stating corrections in-line​aclanthology.org. Another design principle is to let the chatbot prompt and scaffold the learner’s responses more intelligently. Instead of the learner always initiating or struggling to think of what to say, some systems now guide the conversation with cues or suggested questions, especially for lower proficiency users. This kind of scaffolding (e.g. providing conversation topic cards or example phrases) has been shown to help learners engage more and reduce awkward pauses​cambridge.org​cambridge.org. It can also prevent some misunderstandings by keeping the dialogue within manageable bounds.
• Multimodal and Embodied AI: To address the “robotic” feel of AI interlocutors, developers are looking at giving chatbots more human-like qualities. This includes improving speech synthesis – using more natural, emotive text-to-speech voices so the AI’s tone feels engaging​cambridge.org. In addition, embodied conversational agents (avatars or robots) with facial expressions or gestures synchronized to the dialogue might enhance the sense of presence. While still an experimental area, some studies suggest that if a chatbot or voice assistant has a more distinctive persona (a voice and character that learners can relate to), it can increase user engagement​cambridge.org. The hope is that a friendly avatar that smiles or nods (for example) could make the interaction less sterile, and perhaps even convey some empathy or social cues that plain text/voice alone cannot.
• Personalization and Adaptivity: Emerging AI systems aim to adapt to each learner’s needs, which could mitigate several limitations. Personalization may include adjusting the difficulty of vocabulary or grammar in the chatbot’s responses to match the learner’s level (avoiding too many confusing phrases) and tracking individual progress to review frequent errors. Huang et al. (2022) identified personalization as a key affordance of AI tutors​eric.ed.gov, but it’s often underutilized. Future chatbots might analyze a learner’s prior conversations to tailor feedback – for instance, focusing on recurring grammar mistakes with gentle corrections, or remembering context from prior sessions to resume a topic, creating a more continuous and context-rich learning experience.

In academia, these improvements are often discussed alongside teacher-led strategies. Since the AI is not perfect, educators are advised to integrate conversational AI thoughtfully into their curriculum. Ji, Han, & Park (2024) emphasize teacher–student–AI collaboration, where instructors prepare students to get the best out of AI tools (for example, by explaining the AI’s limitations and coaching learners in how to phrase queries or recognize when the AI is wrong)​ scholarspace.manoa.hawaii.edu cambridge.org. This kind of scaffolding can prevent some miscommunications and keep learners from becoming frustrated. As one study put it, providing students with “strategies to prevent interaction disruptions through meaningful negotiation” is important when introducing chatbots into language practice​ cambridge.org. In parallel, ongoing research is establishing design guidelines for AI developers – for instance, Huang et al. (2022) propose design principles to address cognitive load and novelty effects​ eric.ed.gov, and Koc & Savas (2024) call for built-in error correction algorithms to deliver more personalized, immediate feedback​ cambridge.org. The field is moving fast, and with the advent of advanced AI models, many limitations discussed here may be alleviated in the coming years.

Implications for Language Education

The current body of research makes it clear that conversational AI is not a magic bullet for language learning – at least not yet. While these tools offer promising opportunities for additional practice, their technological shortcomings mean that learners and teachers must use them with realistic expectations. Educators implementing AI chatbots or voice assistants in English learning should be aware of their limitations in understanding learner input, correcting mistakes, and carrying out truly natural conversations. It may be wise to use AI as a complement to human instruction, where the chatbot provides extra speaking practice or grammar checking, but teachers still intervene to give nuanced feedback and maintain motivation. Teachers can also help students interpret the AI’s behavior (for example, explaining that if the bot doesn’t understand you, it might be due to pronunciation – and that this is a cue to adjust how you say it).

From a research perspective, the studies reviewed highlight key areas for improvement. Technological advances – especially in speech recognition and large language models – are gradually closing the gap, making AI partners more reliable and context-aware. If these trends continue, the next generation of conversational AI could address many of the pain points identified between 2018 and 2023. However, design and pedagogy will remain important: even a very advanced AI needs to be deployed in a way that aligns with learning principles (e.g. encouraging interaction, providing appropriate feedback, and engaging learners emotionally). The implication for language education is that we should continue experimenting with and refining these AI tools, while also keeping learners’ experience at the center. By understanding the limitations – inaccurate grammar correction, speech recognition errors, contextual misunderstandings, and effects on engagement – we can better support learners in using AI productively. As solutions emerge, language educators can adopt best practices to ensure that conversational AI becomes a truly beneficial partner in the language classroom, rather than a novelty or a source of frustration.

Sources:

• Kukulska-Hulme, A., & Lee, H. (2020). Intelligent assistants in language learning: an analysis of features and limitations. EUROCALL 2020: **Results suggest that while AI assistants can offer conversation and pronunciation practice, these tools often “fail to comprehend meaning or accented pronunciation,” highlighting speech recognition limitations​files.eric.ed.gov. A case study with Amazon Alexa found it understood only ~50% of learners’ utterances, implicitly flagging pronunciation issues when it failed to understand​files.eric.ed.gov.
• Huang, W., Hew, K. F., & Fryer, L. K. (2022). Chatbots for language learning – Are they really useful? A systematic review of chatbot-supported language learning. Journal of Computer Assisted Learning, 38(1): This review of 25 studies identified several challenges in using chatbots, notably technological limitations, the novelty effect, and cognitive load in learners​eric.ed.gov. While chatbots offer timely practice (affordances like 24/7 availability and personalization), their limitations (e.g. lack of error correction and natural dialogue) can hinder long-term effectiveness​eric.ed.gov.
• Koc, F. Ş., & Savaş, P. (2024). The use of AI chatbots in English language learning: A systematic meta-synthesis (2010–2024). ReCALL (Advance online publication): In an analysis of voice-based AI chatbots, the authors report that the top challenges were speech recognition failures (not understanding learner speech), unnatural AI–human interactions, and lack of explicit corrective feedback for errors​cambridge.org. These issues led to learners being “less motivated to converse with chatbots than human partners” as novelty wore off​cambridge.org. Some studies even noted increased anxiety with chatbots due to their lack of empathy and the self-consciousness induced by error corrections​cambridge.org. The review calls for improvements like training ASR on non-native speech​cambridge.org, enhancing chatbot dialogue abilities, and adding immediate error-correction features​cambridge.org. The authors also highlight the potential of new large language models (e.g. GPT-4) to support more open-ended, motivating conversations in the future​cambridge.org.
• Liang, K.-H., Davidson, S., Yuan, X., et al. (2023). ChatBack: Investigating methods of providing grammatical error feedback in a GUI-based language learning chatbot. BEA-2023 Workshop (ACL): This study experimented with real-time grammar correction in a chatbot. It found that existing chatbots “often lack synchronous corrective feedback and self-correction capabilities,” which are known to benefit learning​aclanthology.org. By introducing a system where the chatbot highlighted errors via the interface and prompted learners to self-correct, the researchers observed better outcomes: guided self-correction with metalinguistic hints outperformed explicit correction by the bot​aclanthology.org. The results suggest that delivering feedback through a visual interface (rather than disruptive chat messages) and encouraging learners to fix mistakes can improve the learning experience in AI-mediated chat.
• Godwin-Jones, R. (2022). Chatbots in language learning: AI systems on the rise. EUROCALL 2022 Proceedings: Godwin-Jones provides a commentary on chatbot technology, noting that traditionally “rule-based, scripted” chatbots were favored in CALL for their controlled, predictable interactions​eric.ed.gov. Such design avoids random errors but can be rigid. He argues that newer open-domain AI systems (like those powered by large language models) have the potential to offer more authentic, open-ended conversational practice, aligning with usage-based language learning approaches​eric.ed.gov. The paper suggests hybrid models that retain some instructional structure while leveraging advanced AI for richer dialogue. This perspective highlights a path forward where the spontaneity of AI conversation can be combined with pedagogical control, mitigating contextual misunderstandings.