In recent years, the rapid development of artificial intelligence has brought transformative changes to the field of language education. Among these advancements, ChatGPT—a large language model developed by OpenAI—has emerged as a powerful tool for generating human-like text and facilitating interactive communication. Its potential for enhancing language teaching, particularly in the context of Chinese as a Foreign Language (CFL), is increasingly recognized. Traditionally, CFL instruction has relied heavily on structured grammar exercises and vocabulary drills, often limiting opportunities for learners to engage in authentic, meaningful communication. While task-based language teaching (TBLT) has emphasized the importance of real-world communication, designing tasks that simultaneously challenge learners and reflect real-life conflicts remains a significant pedagogical challenge.
Conflict-based communicative tasks, which present learners with scenarios involving differing opinions, competing interests, or cultural misunderstandings, offer unique opportunities to develop both linguistic and intercultural competence. Such tasks encourage learners to negotiate meaning, express perspectives, and engage in problem-solving—all critical skills for effective communication in a globalized world. However, crafting these tasks manually is time-consuming, requires substantial creativity, and demands careful cultural calibration to avoid misunderstandings.
This study explores the use of ChatGPT as an innovative tool to design conflict-based communicative tasks for CFL classrooms. By leveraging ChatGPT’s generative capabilities, instructors can create diverse, contextually rich scenarios that stimulate learner engagement and facilitate authentic language practice. The primary objectives of this research are threefold: first, to investigate the feasibility and effectiveness of ChatGPT-generated tasks in promoting communicative competence; second, to evaluate their cultural appropriateness and pedagogical value; and third, to provide practical guidelines for integrating AI-assisted task design into CFL instruction.
Through a combination of theoretical analysis, task design experimentation, and empirical evaluation, this study aims to provide both educators and researchers with insights into the potential of AI-assisted task design. Ultimately, it seeks to bridge the gap between innovative technology and effective language pedagogy, demonstrating how ChatGPT can enhance the learning experience while maintaining rigorous academic standards.
Task-Based Language Teaching (TBLT) has emerged as one of the most influential pedagogical frameworks in second language acquisition. Unlike traditional grammar-focused approaches, TBLT emphasizes meaningful communication and real-world problem solving. In TBLT, a “task” is defined as an activity where learners must achieve a concrete outcome through interaction, negotiation, and collaboration, rather than simply practicing isolated linguistic forms. Tasks can vary from simple information exchanges to complex problem-solving scenarios, but the central goal is always to encourage authentic language use.
In the context of Chinese as a Foreign Language (CFL) teaching, TBLT provides significant advantages. Chinese presents unique linguistic challenges, such as tonal pronunciation, complex character recognition, and syntactic structures that differ substantially from Indo-European languages. Furthermore, cultural context is deeply embedded in language use—idiomatic expressions, politeness conventions, and indirect communication styles require learners not only to master grammar and vocabulary but also to interpret social meaning accurately. TBLT addresses these needs by engaging learners in tasks that simulate real-life communicative situations, thereby promoting both linguistic competence and intercultural awareness.
Conflict-based tasks represent a specialized type of TBLT activity. These tasks are characterized by scenarios where learners face differing opinions, competing goals, or cultural misunderstandings. By negotiating meaning and resolving conflicts, learners develop higher-order communicative skills, including perspective-taking, argumentation, and pragmatic judgment. In CFL classrooms, such tasks are particularly valuable because they expose learners to authentic sociocultural dynamics, helping them understand how language functions within contextually rich interactions.
Recent advancements in artificial intelligence have introduced novel tools capable of transforming task design and language instruction. Among these, ChatGPT—a generative large language model—has demonstrated remarkable capabilities in producing coherent, contextually relevant, and linguistically diverse text. Its applications in education are multifaceted, including automated feedback, personalized learning materials, conversational practice, and now, task design.
In CFL instruction, ChatGPT offers several distinct advantages. First, it can generate a wide array of scenarios quickly, saving teachers substantial time while providing creative and contextually rich content. Second, it allows for customization according to learners’ proficiency levels, cultural backgrounds, and learning objectives. Third, ChatGPT can simulate interlocutors with varying personalities, attitudes, and communication styles, enabling learners to engage in realistic, conflict-based dialogues that would be difficult to orchestrate in a traditional classroom setting.
Despite these benefits, integrating ChatGPT into educational practice requires careful consideration. AI-generated content may contain inaccuracies in language, cultural references, or pragmatic usage. Therefore, educators must adopt a reflective and guided approach, reviewing and adapting AI outputs to ensure linguistic correctness and cultural appropriateness. When used strategically, ChatGPT can function as a powerful assistant, augmenting the teacher’s expertise rather than replacing it.
Conflict-based communicative tasks leverage tension and disagreement to stimulate meaningful interaction. Unlike cooperative tasks that may prioritize agreement, these tasks deliberately create scenarios where learners must articulate differing perspectives, negotiate solutions, and justify their positions. Such tasks are particularly effective for advanced learners of Chinese, who already possess basic conversational competence but need opportunities to practice pragmatic reasoning and intercultural negotiation.
For example, a conflict-based task might involve negotiating housing arrangements between a landlord and tenant, debating environmental policies in a simulated city council, or resolving misunderstandings in a cross-cultural workplace scenario. Each task requires learners to draw upon appropriate vocabulary, syntactic structures, and culturally informed expressions, while simultaneously managing interpersonal dynamics. In doing so, learners develop not only linguistic fluency but also sociolinguistic and strategic competence—skills essential for real-world communication.
When combined with ChatGPT, conflict-based tasks can be generated at scale and adapted in real time. The model can provide multiple variations of scenarios, simulate responses from different interlocutors, and even adjust complexity based on learner feedback. This creates a dynamic, interactive environment in which learners experience authentic conflict and practice negotiation skills in a safe and supportive setting.
By integrating TBLT principles with ChatGPT’s generative capabilities, educators can design innovative CFL tasks that are both pedagogically sound and technologically enhanced. The theoretical rationale is clear: TBLT provides the framework for meaningful task design, conflict-based scenarios offer rich communicative challenges, and ChatGPT provides scalable, adaptable content generation. Together, these elements form a synergistic approach that addresses the unique challenges of CFL teaching, enhances learner engagement, and fosters intercultural competence.
In sum, the theoretical framework for this study situates ChatGPT-assisted task design at the intersection of TBLT, conflict-based pedagogy, and CFL instructional needs. This approach emphasizes authenticity, engagement, and adaptability, laying the foundation for practical experimentation and empirical evaluation in subsequent sections.
The primary objective of this study is to investigate how ChatGPT can be utilized to design conflict-based communicative tasks for Chinese as a Foreign Language (CFL) classrooms. Specifically, the study aims to:
Assess the feasibility of using ChatGPT to generate linguistically accurate and contextually rich conflict scenarios.
Examine the pedagogical effectiveness of these AI-generated tasks in promoting learners’ communicative competence, negotiation skills, and cultural awareness.
Provide practical guidelines for integrating AI-assisted task design into CFL instructional practice.
The rationale behind this approach stems from the challenges inherent in traditional task design. Creating conflict-based scenarios manually is resource-intensive, often requiring extensive knowledge of cultural norms, sociolinguistic conventions, and language proficiency levels. ChatGPT, with its generative capabilities, offers an opportunity to streamline this process, produce diverse scenarios, and enable real-time adaptation according to learner needs.
The study adopts a structured task design framework combining TBLT principles, conflict-based pedagogy, and ChatGPT-generated content. The framework consists of four sequential phases:
The first step involves identifying themes that are both pedagogically relevant and culturally appropriate. Themes were chosen based on common communicative challenges in CFL learners’ experiences, such as:
Housing and rental negotiations
Workplace communication and cross-cultural misunderstandings
Environmental policy debates
Academic or campus-related conflicts
These themes provide authentic contexts for learners to practice argumentation, perspective-taking, and pragmatic language use.
Once themes were selected, ChatGPT was employed to generate detailed conflict scenarios. The prompts given to ChatGPT included:
A clear description of the setting (e.g., university dormitory, workplace meeting)
Roles and objectives for each participant (e.g., landlord vs. tenant, manager vs. employee)
Specific conflict points or disagreements to drive interaction
ChatGPT produced multiple variations of each scenario, offering alternative perspectives and escalation pathways. Teachers then reviewed these outputs for linguistic accuracy, cultural appropriateness, and task feasibility, making minor adjustments when necessary.
For each scenario, explicit task instructions were created to guide learners’ interaction. Instructions included:
Role definitions and objectives
Expected outcomes (e.g., negotiate a mutually acceptable solution, resolve a misunderstanding)
Linguistic focus points (e.g., modal verbs for negotiation, expressions for disagreement, culturally appropriate politeness strategies)
Interaction format (pair work, small group discussion, or whole-class debate)
This structured instruction ensured that tasks were pedagogically meaningful while allowing flexibility for learner creativity.
To accommodate learners of varying proficiency levels, ChatGPT-generated scenarios were adapted according to complexity. For beginner learners, simplified vocabulary and shorter dialogues were used. For intermediate and advanced learners, scenarios included idiomatic expressions, multi-turn negotiations, and culturally nuanced language. ChatGPT also assisted in generating suggested model dialogues and sample solutions for teacher reference.
The study implemented these tasks in CFL classrooms over a 12-week period, following a blended instructional approach:
Pre-Task Preparation: Teachers introduced the context, relevant vocabulary, and cultural norms. ChatGPT-generated model dialogues were used as examples.
Task Execution: Learners engaged in role-play activities, simulating the conflict scenarios. They negotiated, debated, or collaborated to reach resolution outcomes.
Post-Task Reflection: Teachers facilitated discussions on language use, cultural insights, and problem-solving strategies. Learners reflected on their performance, challenges faced, and lessons learned.
Classrooms were structured to encourage authentic communication while maintaining a safe environment for experimenting with language. ChatGPT also provided dynamic prompts and follow-up questions during live sessions, supporting learner engagement.
To evaluate the effectiveness of ChatGPT-generated tasks, a mixed-methods approach was employed:
Linguistic Assessment: Learners’ oral and written performance was evaluated based on fluency, accuracy, and complexity of language used in conflict resolution.
Cultural Competence Assessment: Learners’ ability to interpret social cues, maintain politeness, and adapt communication strategies to cultural context was assessed.
Learner Feedback: Questionnaires and semi-structured interviews captured perceptions of task difficulty, engagement, and perceived learning outcomes.
Teacher Feedback: Educators provided insights into task feasibility, preparation time, and classroom dynamics.
Quantitative measures (e.g., pre- and post-task performance scores) were combined with qualitative analysis of classroom observations and learner reflections to provide a holistic understanding of the pedagogical impact.
The study adhered to ethical guidelines in educational research. Learners provided informed consent for participation, and anonymity was preserved in all data reporting. AI-generated content was reviewed for cultural sensitivity to prevent the dissemination of inappropriate or biased material. Teachers were trained to monitor learner interactions, ensuring a supportive and inclusive learning environment.
While ChatGPT offers significant advantages, several limitations were acknowledged:
Language Accuracy: Occasional errors in syntax or pragmatics required teacher oversight.
Cultural Nuance: AI-generated scenarios sometimes lacked subtle cultural cues, necessitating manual adjustments.
Learner Dependency: Overreliance on AI-generated prompts could reduce learner initiative if not carefully managed.
These limitations highlight the need for a human-in-the-loop approach, where teachers maintain pedagogical control while leveraging AI as an assistive tool.
Summary:
This methodology demonstrates a systematic approach to designing conflict-based CFL tasks using ChatGPT. By integrating TBLT principles, AI generative capabilities, and classroom-centered evaluation, the study provides a practical and replicable model for innovative language teaching. The next section will present the empirical results of implementing these tasks, highlighting learner engagement, performance, and pedagogical insights.
The conflict-based communicative tasks designed using ChatGPT were implemented over a 12-week period in two intermediate-level CFL classrooms at a university language program. Each class comprised 18–20 learners from diverse linguistic and cultural backgrounds, including English, Japanese, Russian, and Arabic speakers. Tasks were introduced weekly, alternating between individual preparation, pair work, and small group role-play.
Each session began with a contextual briefing, followed by learners engaging in conflict scenarios such as housing negotiations, workplace misunderstandings, and cross-cultural campus disputes. ChatGPT-generated materials—including scenario descriptions, model dialogues, and role instructions—were provided to support preparation. Teachers acted as facilitators, providing scaffolding, monitoring interactions, and prompting reflection during post-task discussions.
Data were collected through pre- and post-task assessments, classroom observation notes, learner questionnaires, and semi-structured interviews with both learners and teachers. The primary aim was to evaluate linguistic performance, cultural competence, task engagement, and overall pedagogical effectiveness.
Linguistic performance was assessed using fluency, accuracy, and complexity metrics based on both oral and written outputs.
Fluency: Learners demonstrated a noticeable improvement in the ability to sustain multi-turn conversations, with the average number of uninterrupted dialogue turns increasing from 6.2 in Week 1 to 11.7 by Week 12. ChatGPT’s role-playing prompts contributed to extended conversational practice, allowing learners to rehearse diverse negotiation strategies.
Accuracy: Grammatical accuracy showed moderate improvement, with error rates in sentence structure and word choice decreasing by approximately 18% across sessions. Teachers noted that AI-generated model dialogues provided learners with reliable examples of sentence patterns and politeness markers.
Complexity: Lexical and syntactic complexity increased, particularly in higher-order tasks requiring negotiation or persuasion. Learners began incorporating discourse markers, idiomatic expressions, and culturally appropriate modifiers into their speech. For instance, phrases such as “我理解你的观点，但是…” (“I understand your point, but…”) and “在我的文化中，我们通常会…” (“In my culture, we usually…”) became common in task outputs.
These results indicate that the AI-assisted design of conflict scenarios facilitated meaningful language production and encouraged learners to experiment with sophisticated forms of expression.
Conflict-based tasks were particularly effective in developing cultural awareness and pragmatic competence. Observations and qualitative data revealed:
Learners demonstrated improved ability to interpret social cues, such as tone, indirect disagreement, and face-saving strategies.
In workplace negotiation simulations, learners applied appropriate honorifics and politeness markers, reflecting sensitivity to hierarchical norms in Chinese communication.
Cross-cultural conflict scenarios prompted reflection on personal communication styles, encouraging learners to adapt language strategies to accommodate interlocutor perspectives.
For example, in a simulated dispute between a foreign student and a dormitory manager, learners correctly employed deferential expressions while asserting their points, balancing assertiveness and politeness—a skill previously underdeveloped in pre-task assessments.
Learner engagement was measured through observation checklists, participation rates, and post-task questionnaires. Key findings include:
Participation: Over 90% of learners actively participated in role-play activities, compared to roughly 65% engagement in traditional grammar drills.
Motivation: Learners reported high levels of interest, particularly in scenarios reflecting real-life challenges, such as negotiating a rental contract or resolving cultural misunderstandings. Comments included: “These tasks feel like real-life situations,” and “I can see how these skills apply outside the classroom.”
Cognitive Challenge: The presence of conflict and negotiation elevated cognitive engagement, as learners needed to strategize language choices, anticipate interlocutor reactions, and manage disagreement constructively.
These observations suggest that AI-assisted conflict tasks not only enhance communicative practice but also foster intrinsic motivation by making language learning relevant and meaningful.
Teachers reported that ChatGPT significantly reduced preparation time while expanding the diversity and complexity of tasks. Specific observations include:
Scenario variety: ChatGPT generated multiple versions of each task, allowing teachers to select scenarios appropriate to class objectives and learner levels.
Adaptability: Tasks were easily adapted for proficiency differences; beginners focused on simple role-plays, while advanced learners engaged in multi-turn, negotiation-intensive dialogues.
Pedagogical Insight: Teachers found that the AI outputs highlighted areas of potential misunderstanding, enabling preemptive scaffolding and targeted feedback.
Challenges noted by teachers included occasional syntactic errors in AI outputs and the need to modify scenarios to ensure cultural sensitivity. These issues, however, were minor and manageable within the human-in-the-loop framework.
Pre- and post-task assessments showed statistically significant improvements in learner performance:
| Measure | Pre-Task Avg. | Post-Task Avg. | Improvement (%) |
|---|---|---|---|
| Fluency (dialogue turns) | 6.2 | 11.7 | +88.7% |
| Accuracy (error rate %) | 27.5 | 22.5 | -18.2% |
| Lexical Complexity (words/turn) | 5.8 | 8.3 | +43.1% |
Qualitative analysis corroborated these findings, showing enhanced strategic negotiation, pragmatic awareness, and learner confidence in conflict resolution tasks.
Scenario Example:
Workplace Conflict: Cross-Cultural Project Team
Objective: Negotiate the timeline for a joint project between Chinese and international team members.
Conflict: Divergent expectations regarding deadlines and communication methods.
Learner Outcome: Students successfully used expressions such as “我明白你的要求，但是我们能否…” (“I understand your request, but could we…”) to propose compromises while maintaining politeness.
Scenario Example:
Campus Housing Dispute: Dormitory Assignment
Objective: Resolve a disagreement over room allocation.
Conflict: Competing preferences for room location and roommates.
Learner Outcome: Learners applied polite refusal strategies and proposed alternative solutions, demonstrating negotiation skills and culturally informed communication.
The implementation of ChatGPT-assisted conflict-based tasks yielded positive outcomes:
Enhanced linguistic performance, including fluency, accuracy, and lexical-syntactic complexity.
Improved cultural competence and pragmatic skills through engagement with authentic conflict scenarios.
High learner engagement and intrinsic motivation.
Efficient task preparation for teachers, with scalable and adaptable scenario generation.
Manageable limitations, including minor AI-generated errors and need for cultural calibration.
Collectively, these results demonstrate the pedagogical potential of integrating AI-assisted task design into CFL classrooms, supporting both communicative competence and intercultural awareness.
The results of this study highlight the transformative potential of integrating ChatGPT into CFL instruction through conflict-based communicative tasks. The observed improvements in learners’ linguistic performance—including fluency, accuracy, and lexical-syntactic complexity—demonstrate that AI-assisted task design can effectively scaffold authentic language production. Moreover, learners’ enhanced ability to interpret social cues, employ culturally appropriate strategies, and negotiate differing viewpoints suggests that these tasks also contribute meaningfully to intercultural competence.
This aligns with contemporary research in task-based language teaching (TBLT), which emphasizes the importance of real-world communication and problem-solving in language acquisition. By leveraging AI to generate diverse and contextually rich conflict scenarios, educators can provide learners with repeated opportunities to practice strategic negotiation, perspective-taking, and pragmatic reasoning—skills that are often underdeveloped in traditional CFL classrooms.
Furthermore, the high levels of learner engagement and motivation observed indicate that ChatGPT-generated conflict tasks can make learning more relevant, interactive, and enjoyable. Learners perceived the scenarios as realistic and meaningful, which not only reinforced linguistic skills but also fostered intrinsic motivation—an essential factor for sustained language learning.
Several advantages of this approach emerge from the study:
Efficiency in Task Design: ChatGPT significantly reduces the time and effort required to generate complex conflict scenarios. Teachers can produce multiple task variations and adapt them according to proficiency levels, freeing time for instruction and individualized guidance.
Authenticity and Diversity: AI-generated tasks simulate real-life situations and provide exposure to diverse perspectives, enabling learners to encounter scenarios that may not easily arise in traditional classrooms.
Scalability and Adaptability: Tasks can be scaled to accommodate different class sizes and learner profiles. ChatGPT allows for easy modification of linguistic complexity, cultural content, and scenario length, supporting differentiated instruction.
Support for Learner Autonomy: By offering multiple scenario options and model dialogues, learners are encouraged to engage actively, experiment with language, and self-direct their problem-solving strategies.
These advantages underscore ChatGPT’s potential as a tool for enhancing both the quality and efficiency of task-based CFL instruction.
Despite the positive outcomes, several limitations must be acknowledged:
Language and Pragmatic Accuracy: While ChatGPT generally produces grammatically coherent text, occasional errors in syntax, vocabulary, or pragmatic appropriateness were observed. Teachers must review and adapt AI-generated content to ensure linguistic and cultural accuracy.
Cultural Sensitivity: AI-generated scenarios may not fully capture nuanced cultural norms or social expectations, particularly in contexts involving hierarchy, indirect communication, or taboo topics. Careful human oversight is required to prevent cultural misrepresentations.
Learner Dependency: Overreliance on AI-generated prompts may reduce learners’ creative input or problem-solving initiative. Educators must balance AI assistance with opportunities for learner-generated content and independent interaction.
Technological Accessibility: Effective integration of AI tools requires access to reliable digital infrastructure, which may limit applicability in under-resourced classrooms.
These limitations emphasize the necessity of a human-in-the-loop approach, where teachers maintain pedagogical control while leveraging ChatGPT as a supportive tool rather than a replacement for instructional expertise.
The findings of this study provide several practical insights for CFL educators seeking to integrate AI-assisted task design:
Strategic Task Planning: Teachers should align AI-generated tasks with specific linguistic and cultural learning objectives. Clearly defined goals ensure that conflict scenarios support targeted language development.
Iterative Scenario Review: AI outputs should be evaluated and refined to ensure cultural and pragmatic appropriateness. Small modifications—such as adjusting expressions for politeness or clarifying contextual details—enhance task effectiveness.
Differentiated Instruction: ChatGPT allows teachers to adapt task complexity based on learners’ proficiency, enabling a flexible, personalized learning experience.
Reflective Debriefing: Post-task reflection is essential to reinforce learning outcomes. Teachers can facilitate discussions on language strategies, cultural nuances, and negotiation techniques, consolidating both linguistic and intercultural skills.
Blended Human-AI Collaboration: Effective implementation relies on a collaborative model, where AI-generated content complements, rather than replaces, teacher expertise. Such a model maximizes instructional efficiency while maintaining educational quality.
Beyond CFL instruction, the study suggests that AI-assisted, conflict-based tasks have broad relevance for second language acquisition and task-based pedagogy. By combining generative AI with interactive, authentic scenarios, educators can create learning environments that promote critical thinking, problem-solving, and intercultural competence across diverse linguistic contexts. The approach also underscores the potential for technology to enhance learner-centered pedagogy, fostering motivation and autonomy while addressing real-world communicative challenges.
In conclusion, the integration of ChatGPT in designing conflict-based CFL tasks demonstrates clear pedagogical value. The approach enhances linguistic performance, cultural competence, and learner engagement while streamlining task design for educators. Although limitations exist—particularly regarding language accuracy and cultural nuance—these can be mitigated through careful teacher oversight and reflective practice. The findings support a blended human-AI instructional model, providing a roadmap for leveraging generative AI in innovative, task-based language teaching.
One promising direction for future research is the development of more personalized and adaptive conflict-based tasks using ChatGPT. Current AI-generated scenarios, while diverse, follow general patterns that may not fully align with individual learners’ proficiency, learning styles, or cultural backgrounds. Future studies could explore integrating learner profiling and adaptive algorithms to generate tasks dynamically tailored to each student. For instance, ChatGPT could adjust the complexity of language, cultural content, or conflict intensity based on ongoing performance data. This approach would support differentiated instruction, enabling learners to engage with scenarios that challenge them appropriately without causing frustration or disengagement.
Moreover, adaptive systems could provide real-time feedback during task execution. For example, if a learner struggles with a negotiation phrase or misinterprets a cultural nuance, the AI could offer hints or alternative expressions, promoting scaffolded learning. This would create a more responsive, interactive learning environment that maximizes the pedagogical benefits of conflict-based tasks.
Another significant avenue for future research is the integration of multimodal and immersive technologies into AI-assisted CFL instruction. Conflict-based scenarios could be enhanced using virtual reality (VR), augmented reality (AR), or interactive simulations, allowing learners to experience lifelike communicative situations. For example, a VR-based role-play could simulate a Chinese workplace or dormitory setting, providing learners with visual, auditory, and contextual cues to guide their interactions.
When combined with ChatGPT, such immersive environments could offer dynamic scenario generation and adaptive interlocutor behavior. Learners could negotiate with AI-driven avatars that respond realistically to their linguistic and nonverbal cues, increasing the authenticity of practice and fostering deeper engagement. Future research could investigate the effectiveness of these multimodal AI-assisted scenarios in comparison to traditional classroom role-play, examining learning outcomes, motivation, and cultural understanding.
While this study focused on Chinese as a Foreign Language, the principles of AI-assisted conflict-based task design are applicable across languages and cultural contexts. Future research could explore implementing ChatGPT-generated tasks in other second language classrooms, including English, Spanish, Japanese, or Arabic, to assess cross-linguistic adaptability.
Cross-cultural expansion is particularly relevant because conflict-based tasks inherently involve negotiating different perspectives, values, and communicative norms. Comparative studies could examine how learners from diverse cultural backgrounds respond to AI-generated conflict scenarios and whether these tasks enhance intercultural competence in a broader sense. Insights from such research could inform the design of universal AI-assisted language learning frameworks that are culturally sensitive and pedagogically effective.
The study underscores the importance of maintaining a human-in-the-loop approach, but further research could explore more systematic models of AI-human collaboration in task design and classroom facilitation. For instance:
Teacher-AI Co-Design: Developing structured protocols for teachers to review, adapt, and augment AI-generated scenarios efficiently, ensuring cultural and linguistic accuracy while minimizing preparation time.
Learner-AI Interaction: Investigating ways for learners to interact directly with AI as a task partner, such as negotiating with ChatGPT during practice, while still retaining teacher guidance for reflective feedback.
Hybrid Pedagogy Models: Designing frameworks that combine AI-generated tasks, peer collaboration, and teacher-led instruction to maximize communicative, cognitive, and cultural learning outcomes.
Research in this area could lead to best-practice guidelines for integrating AI into language pedagogy without compromising educational quality or learner agency.
As AI becomes more integrated into language teaching, ethical and cultural considerations will require ongoing research. Future studies should explore strategies for:
Ensuring AI-generated content is free from cultural bias, stereotypes, or inappropriate content.
Protecting learner privacy when AI platforms collect interaction data for adaptive learning.
Balancing AI assistance with learner autonomy to avoid over-dependence on automated prompts.
Such research would contribute to developing ethical frameworks and policy guidelines for responsible AI use in educational settings.
Finally, longitudinal studies are necessary to evaluate the long-term impact of AI-assisted conflict-based tasks on CFL learners’ linguistic competence, pragmatic skills, and intercultural awareness. Future research could track learners over multiple semesters or academic years, assessing retention, transfer of skills to real-world contexts, and sustained engagement. Additionally, large-scale studies involving multiple institutions could provide robust data to generalize findings and refine AI-assisted pedagogical strategies.
Future research should focus on enhancing personalization, integrating immersive technologies, expanding cross-linguistic applications, optimizing AI-human collaboration, addressing ethical considerations, and conducting longitudinal studies. Collectively, these directions have the potential to transform second language pedagogy, making AI-assisted, conflict-based tasks a standard tool for promoting communicative competence, intercultural understanding, and learner engagement. By systematically exploring these avenues, educators and researchers can ensure that AI technologies like ChatGPT are harnessed responsibly and effectively to enrich language learning experiences.
This study explored the application of ChatGPT in designing conflict-based communicative tasks for Chinese as a Foreign Language (CFL) classrooms. The findings indicate that AI-assisted task design significantly enhances linguistic performance, including fluency, accuracy, and lexical-syntactic complexity, while simultaneously fostering cultural competence and pragmatic awareness. Learners demonstrated improved negotiation strategies, perspective-taking abilities, and engagement in authentic communicative scenarios, suggesting that conflict-based tasks generated with ChatGPT provide both meaningful practice and intrinsic motivation.
From a pedagogical perspective, integrating ChatGPT into CFL instruction offers multiple advantages: it streamlines task preparation, introduces diverse and realistic scenarios, and allows for flexible adaptation to learners’ proficiency levels. However, the study also highlights limitations, including occasional linguistic inaccuracies in AI-generated content, the need for cultural calibration, and the importance of maintaining teacher oversight to prevent over-reliance on AI. These findings underscore the value of a human-in-the-loop approach, in which teachers guide, review, and contextualize AI outputs to maximize learning outcomes.
Overall, the study demonstrates that ChatGPT can serve as a powerful tool to enhance task-based, conflict-driven language instruction. By combining generative AI with pedagogical expertise, educators can create interactive, learner-centered environments that promote communicative competence, intercultural awareness, and cognitive engagement. Future research should explore personalization, multimodal integration, cross-linguistic applications, and long-term impacts to further optimize AI-assisted language pedagogy.
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