Conversational Education at Scale: A Multi- LLM Agent Workflow for Procedural Learning and Pedagogic Quality Assessment (2025)

Abstract: Large language models (LLMs) have advanced virtual educators and learners, bridging NLP with AI4Education. Existing work often lacks scalability and fails to leverage diverse, large-scale course content, with limited frameworks for assessing pedagogic quality. To this end, we propose WikiHowAgent, a multi-agent workflow leveraging LLMs to simulate interactive teaching-learning conversations. It integrates teacher and learner agents, an interaction manager, and an evaluator to facilitate procedural learning and assess pedagogic quality. We introduce a dataset of 114,296 teacher-learner conversations grounded in 14,287 tutorials across 17 domains and 727 topics. Our evaluation protocol combines computational and rubric-based metrics with human judgment alignment. Results demonstrate the workflow’s effectiveness in diverse setups, offering insights into LLM capabilities across domains. Our datasets and implementations are fully open-sourced.

## introduction large language models (llms) have sparked smart teaching and learning, reshaping education and bridging nlp with ai4education @xcite . llms have been employed as single agents in education, serving as teachers @xcite , learners @xcite , or evaluators @xcite to enhance personalized learning @xcite . specifically, multi-llms have shown promise in simulating teacher-learner interactions, assisting competitive problem solving @xcite , providing personalized feedback @xcite , immersive multimodal training @xcite , and evaluating educational content @xcite . however, none of the existing approaches use diverse and large-scale course content, which is vital for capturing the variability in instructional materials. and they lack a comprehensive evaluation for assessing pedagogic quality, limiting insights into the effectiveness of teaching-learning interactions @xcite . to this end, we introduce a multi-agent workflow, namely wikihowagent (see figure 1 ), leveraging llms to simulate an interactive teachinglearning conversation graph. it utilizes llmpowered teacher and learner agents, processes instructional tutorials, and generates guided conversations in an interactive mode. the workflow is designed to facilitate a seamless interaction between the teacher and learner agents, allowing for a natural flow of conversation. the workflow consists of four main components: (1) teacher agent, which provides instructions, answers clarification questions, and guides tutorial progression. we seek the answers to the following research questions: (rq1) how effective is the proposed multi-agent workflow with diverse llms? how does it perform in homogeneous learning (the same llm for teacher, learner, and evaluator) versus heterogeneous learning (different llms for learner given the same teacher)? (rq2) how do llms perform across various domains, and what insights can be drawn about their strengths and limitations in specific areas of procedural learning? (rq3) how well do the diverse automatic evaluation metrics align with human judgment in assessing pedagogic quality and conversational performance? we list our key contributions as follows: • a large-scale dataset consisting of 114,296 teacher-learner conversations grounded in 14,287 tutorials. it spans 17 domains and 727 topics, organized in a hierarchical knowledge graph. ## multi-llm agent procedural learning as shown in figure 1 , we propose a multi-llm agent workflow designed for procedural learning through interactive dialogue. algorithm 1 outlines the implementation of the workflow, comprising a teacher agent, a learner agent, and an evaluator agent, to dynamically simulate guided tutorial sessions using a conversational graph and assess pedagogic quality. first, we initialize the learning state and conversation graph, where each node represents a conversational turn, and edges represent interactions between the teacher and learner algorithm 1 multi-llm agent workflow for procedural learning require: tutorial @xmath0, t2, . . . , tn], max interactions m 1: initialize state: 2: s ← {messages: [], tutorial: t , current_step: 0, needs_clarification: false, finished: false} 3: define nodes: 4: teachernode: s ← teacher_agent(s) 5: learnernode: s ← learner_agent(s) 6: simulate a conversation by iterating through the nodes in the graph: 7: while ¬s.finished and |s.messages| < m do 8: if s.@xmath1: s ← teachernode(s) ▷ provide first instruction 10: else if s.messages[-1] ∈ teacher then 11: s ← learnernode(s) ▷ generate learner response 12: if learner asks a question and not mention "next step" then 13: s.needs_clarification ← true ▷ set flag for teacher 14: else 15: s.current_step ← s.current_step + 1 ▷ next learning step 16: end if 17: else 18: s ← teachernode(s) ▷ provide teacher instruction 19: if s.needs_clarification then 20: s.needs_clarification ← false ▷ reset flag after clarification 21: end if 22: end if 23: if s.@xmath2: s.finished ← true ▷ end conversation if tutorial is complete 25: end if 26: end while 27: evaluate conversation: 28: e ← evaluator(s.messages) 29: return s.messages, e agents. then, we define the nodes, invoking either the teacher agent or the learner agent: the teacher agent is responsible for providing instructions and answering clarification questions. the learner agent simulates a learner's understanding and generates follow-up questions or responses. next, we simulate the conversation by iterating through the nodes in the graph: (1) it begins with the teacher agent providing instructions based on the first-step's tutorial content. (2) the interaction manager monitors the conversation state, tracks tutorial progress, handles clarification needs, and determines the next node in the graph. (3) it continues iteratively, with the teacher agent providing instructions and the learner agent generating responses until the tutorial is completed or a maximum interaction limit is reached. last, the evaluator assesses the generated conversation using a range of evaluation metrics, including computational metrics and rubric-based metrics facilitated by an llm-based evaluator agent. ## results we conduct experiments with 8 llms from diverse providers: the open-source models including deepseek (7b), qwen2 (7b), gemma (7b), olmo2 (7b), openchat (7b), llama3 (8b), phi4 (14b), and commerical gpt-4 (1.76tb). ## discussion implication. this work introduces a modular, multi-agent workflow for procedural learning that contributes to the development of scalable, llm-based educational systems. the proposed largescale dataset, grounded in structured tutorials and spanning domains, offers a valuable resource for both nlp and ai4education communities. combined with a multi-perspective evaluation protocol (automatic metrics, rubric scoring, and human assessment), the workflow enables comprehensive assessment of llms in educational contexts. from the perspectives of educational ai systems, this approach supports the development of scalable, automated tutoring systems adaptable to various topics, instructional goals, and learner profiles. by simulating interactive teacher-learner dialogues, it moves beyond static benchmarks and enables dynamic, pedagogically grounded evaluation of llms. this opens new directions for ai-powered curriculum design, feedback systems, and intelligent tutoring. from a broader societal perspective, such systems can improve access to high-quality instruction, particularly in resource-limited settings. meanwhile, their growing role also highlights the need for responsible design, transparent evaluation, and human oversight to ensure they complement rather than replace effective teaching. positioning future work. looking ahead, several directions can further advance this work. first, incorporating real human learners and evaluators is essential to capture authentic learning dynamics and validate automated evaluation methods. second, future research can focus on explicitly modeling pedagogical skills within llms, such as scaffolding, adaptive explanation, and constructive feedback, to more accurately reflect teaching competence and align llms with pedagogical skills. lastly, enhancing personalization through learner modeling and adaptive dialogue strategies will improve responsiveness and realism, bringing the sys-tem closer to real-world deployment. ## conclusion in this work, we introduced a multi-llm agent workflow for simulating procedural learning and assessing pedagogic quality at scale. our framework leverages teacher, learner, and evaluator agents to enable dynamic teaching-learning simulations grounded in diverse instructional content. we presented a large-scale dataset and a comprehensive evaluation protocol, demonstrating the workflow's effectiveness across various setups. we also identify areas for improvement, such as enhancing learner engagement and aligning automated evaluations with human judgments. this work lays the foundation for scalable, adaptive educational systems, bridging nlp with ai4education.