LLMs in Education: Novel Perspectives, Challenges, and Opportunities (2409.11917v1)

Abstract: The role of LLMs in education is an increasing area of interest today, considering the new opportunities they offer for teaching, learning, and assessment. This cutting-edge tutorial provides an overview of the educational applications of NLP and the impact that the recent advances in LLMs have had on this field. We will discuss the key challenges and opportunities presented by LLMs, grounding them in the context of four major educational applications: reading, writing, and speaking skills, and intelligent tutoring systems (ITS). This COLING 2025 tutorial is designed for researchers and practitioners interested in the educational applications of NLP and the role LLMs have to play in this area. It is the first of its kind to address this timely topic.

• The paper provides a comprehensive framework for applying LLMs in education, addressing writing assistance, reading support, spoken language assessment, and intelligent tutoring systems.
• It explores practical techniques such as zero-shot and prompt-tuning for tasks like grammatical error correction and text simplification, analyzing trade-offs in performance and efficiency.
• The research highlights opportunities for personalized learning and global accessibility while addressing ethical concerns, evaluation challenges, and resource demands.

This paper, "LLMs in Education: Novel Perspectives, Challenges, and Opportunities" (2409.11917), provides a comprehensive overview of the impact of LLMs on educational applications, targeting researchers and practitioners interested in the intersection of NLP and education. It explores the practical applications, challenges, and opportunities presented by LLMs across four key areas: writing assistance, reading assistance, spoken language learning and assessment, and Intelligent Tutoring Systems (ITS).

LLMs for Writing Assistance

The tutorial focuses on Grammatical Error Correction (GEC) as a prime example of writing assistance. Implementing GEC systems with LLMs offers pedagogical benefits like instant feedback and personalized learning profiles.

• Overview of GEC: The paper covers the history of GEC, common datasets (e.g., CoNLL-2014, JFLEG [Yannakoudakis et al., 2011; Napoles et al., 2017]), evaluation methods (e.g., M² metric, F-score [Dahlmeier and Ng, 2012; Felice and Briscoe, 2015; Grundkiewicz et al., 2015]), and techniques from rule-based to neural models (e.g., GECTOR [Kochmar et al., 2012; Alhafni et al., 2023; Omelianchuk et al., 2020]).
• LLMs for GEC: Practical implementation involves using LLMs through various prompting techniques (e.g., zero-shot, few-shot prompting) to identify and correct errors [Loem et al., 2023]. This can be done by providing instructions to the LLM to act as a GEC system or by showing examples of errors and their corrections. Evaluation metrics like fluency and coherence, alongside traditional F-score, are used to assess performance [Fang et al., 2023; Raheja et al., 2024; Katinskaia and Yangarber, 2024]. Comparing LLMs to fine-tuned supervised models reveals trade-offs in performance and controllability [Omelianchuk et al., 2024]. LLMs can also be used as evaluators for GEC systems [Kobayashi etachi al., 2024] or to generate explanations for edits, which is crucial for pedagogical applications [Cortez et al., 2024; Kaneko and Okazaki, 2024].
• Implementation Considerations: Directly prompting large, general-purpose LLMs can be computationally expensive for real-time feedback. Fine-tuning smaller models on GEC datasets or using prompt-tuning might offer better efficiency. The quality and consistency of LLM output can vary depending on the model and prompting strategy. Ensuring comprehensive error coverage across different error types is a challenge.
• Future Directions: Focus areas include developing better evaluation methods that align with pedagogical goals, improving usability for learners, and enhancing the interpretability of LLM suggestions.

LLMs for Reading Assistance

This section covers Readability Assessment (RA) and Text Simplification (TS), both vital for helping learners access and comprehend text.

• Overview of RA and TS: Traditional approaches involved linguistic features and supervised models. Challenges include domain-specific variations and multilingual support [Vajjala, 2022; Alva-Manchego et al., 2020].
• LLMs for Reading Support: LLMs offer new avenues, starting with zero-shot applications where a prompt instructs the LLM to simplify text or assess its complexity [Kew et al., 2023]. More advanced methods involve prompt-tuning or fine-tuning LLMs on specific readability or simplification datasets to tailor their output [Lee and Lee, 2023]. Personalization, where simplification is adapted to individual learner needs, is a promising application [Tran et al., 2024].
• Implementation Considerations: Implementing LLM-based simplification requires carefully crafted prompts to ensure meaning preservation while reducing complexity [Agrawal and Carpuat, 2024]. For readability assessment, LLMs can analyze text and provide a complexity score or qualitative feedback based on prompting. Personalization adds complexity, requiring user modeling to adapt the output. Handling diverse text genres and multilingual simplification are ongoing challenges [Shardlow et al., 2024].
• Future Directions: Developing user-based evaluation methods that measure actual comprehension gains is critical [Vajjala and Lučić, 2019; Säuberli et al., 2024]. Expanding support for more languages and exploring how LLMs can generate explanations for complex concepts are also key directions.

LLMs for Spoken Language Learning and Assessment

Assessing and providing feedback on spoken language proficiency is a complex task where LLMs, combined with speech technologies, show potential.

• Overview: The field has evolved from rule-based systems to deep neural networks for holistic and analytic assessment [Zechner and Evanini, 2019]. Key tasks include pronunciation assessment, spoken Grammatical Error Detection (GED), and correction (GEC) [Bannò et al., 2022; Lu et al., 2020].
• LLMs for Speaking Assessment and Feedback: Practical approaches often involve transcribing spoken input using powerful ASR models like Whisper [Radford et al., 2022] and then processing the transcription with text-based LLMs (like BERT or general LLMs) for assessment (holistic or analytic) or GEC [Craighead et al., 2020; Raina et al., 2020; Wang et al., 2021]. Speech foundation models like wav2vec 2.0 or HuBERT can be used directly for mispronunciation detection and pronunciation assessment [Baevski et al., 2020; Hsu et al., 2021; Peng et al., 2021; Kim et al., 2022; Bannò and Matassoni, 2023]. Combining these, recent work explores end-to-end spoken GEC using models like Whisper [Bannò et al., 2024a]. LLMs can also provide analytic assessment based on transcriptions [Bannò et al., 2024b].
• Implementation Considerations: A core challenge is the accuracy of ASR, especially for non-native speech, as errors in transcription can propagate to downstream LLM processing. Real-time feedback requires efficient processing pipelines integrating ASR and LLM inference. Providing detailed, actionable feedback beyond a score is also challenging.
• Future Directions: Integrating multimodal models (processing both speech and text/semantics) like SALMONN or Qwen Audio is a promising direction [Tang et al., 2024; Chu et al., 2023]. Utilizing Text-to-Speech (TTS) models like Bark or Voicecraft can allow LLMs to provide spoken feedback or create practice materials [Schumacher et al., 2023; Peng et al., 2024].

LLMs in Intelligent Tutoring Systems (ITS)

ITS aim to provide personalized, one-on-one tutoring experiences, mirroring the effectiveness of human tutors [Bloom, 1984]. LLMs are transforming ITS development.

• Overview of ITS: Traditional ITS relied on explicit computational models of learning, student knowledge, and pedagogical strategies (e.g., model-tracing, constraint-based modeling) [Graesser et al., 2001; Mitrovic, 2005]. These were often domain-specific, particularly for STEM subjects [Kochmar et al., 2022].
• LLMs in ITS Development: LLMs can power various ITS functionalities, including:
  • Question Solving & Error Correction: LLMs can solve problems, identify student errors, and suggest corrections [Amini et al., 2019; Balse et al., 2023].
  • Confusion Resolution: Generating explanations or rephrasing concepts based on student difficulties [Balse et al., 2023].
  • Content and Question Generation: Creating explanations, examples, practice problems, and quizzes automatically [Grenander et al., 2021; Elkins et al., 2024; Li et al., 2024].
  • Dialogue Management: Simulating tutoring conversations, responding to student queries, and guiding them through material [Paladines and Ramirez, 2020].
  • Simulating Agents: LLMs can simulate student behavior for training human tutors or evaluating ITS [Markel et al., 2023; Wang and Demszky, 2023].
  • Data Augmentation: Generating synthetic student interactions or problem variations to create data for fine-tuning models [Vasselli et al., 2023].
• Implementation Approaches: Building LLM-powered ITS often involves prompt-based techniques to guide the conversation and ensure pedagogical soundness [Wang et al., 2024a]. Modular prompting, breaking down the tutoring process into smaller steps, can improve control and reliability [Daheim et al., 2024]. Fine-tuning LLMs on educational dialogue datasets (like MathDial [Macina et al., 2023] or CIMA [Stasaski et al., 2020]) helps them specialize in tutoring interactions.
• Implementation Challenges: Ensuring factual accuracy and pedagogical effectiveness of LLM-generated content is crucial. Maintaining coherent and consistent tutoring dialogues over time is difficult. Tailoring interactions to diverse student needs, learning styles, and prior knowledge requires sophisticated user modeling. Scalability and computational resources for serving potentially many simultaneous tutoring sessions are practical concerns. Data scarcity for fine-tuning specialized educational LLMs remains a challenge [Macina et al., 2023; Wang et al., 2024a; Stasaski et al., 2020].
• Future Directions: Key areas for research include developing standardized evaluation benchmarks specifically for LLM-driven ITS, creating larger and more diverse public educational datasets, and exploring the possibility of developing specialized foundational LLMs specifically trained for educational tasks. Investigating the long-term learning outcomes and potential equity/bias issues of LLM-powered ITS is essential.

General Challenges and Opportunities

Across all educational applications, LLMs present common challenges and opportunities.

• Opportunities:
  • Personalization: LLMs can tailor content, feedback, and tutoring interactions to individual learner needs.
  • Accessibility: Potential to provide education and support to learners worldwide, regardless of location or teacher availability.
  • New Capabilities: Enabling complex tasks like generating detailed explanations, simulating dialogues, and providing nuanced feedback that were previously difficult with traditional NLP methods.
• Challenges:
  • Evaluation: Developing robust and pedagogically valid evaluation metrics that go beyond surface-level performance to measure actual learning gains and user experience.
  • Ethics and Bias: Addressing potential biases in LLM outputs, ensuring fairness across different learner demographics, and maintaining data privacy and security.
  • Reliability and Accuracy: Ensuring LLMs provide factually correct information and pedagogically sound guidance, especially in sensitive subjects. Hallucinations and inconsistent outputs remain risks.
  • Interpretability: Understanding why an LLM produces a certain response or assessment is important for building trust and providing transparent feedback.
  • Data Scarcity: Lack of large-scale, high-quality educational datasets (especially multilingual and multimodal) for training and fine-tuning.
  • Resource Requirements: Running large LLMs can be computationally expensive.

The paper highlights that integrating LLMs into education is a high-impact area with significant potential but requires careful consideration of technical, pedagogical, and ethical challenges. The tutorial format suggests a focus on practical demonstrations and discussions relevant to implementation in real-world educational settings.