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<span style="font-size: 80%">COLING 2025 Tutorial</span><br />
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LLMs in Education: Novel Perspectives, Challenges, and Opportunities
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<span class="author-block"><sup>1</sup>New York University,</span>
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<span class="author-block"><sup>3</sup>University of Cambridge,</span>
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<h2 class="title is-3">About this tutorial</h2>
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<p>
The role of large language models (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 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.
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<h2 class="title is-3">Schedule</h2>
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<th class="tg-0pky">Time</th>
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<td class="tg-0lax">9:00—9:15</td>
<td class="tg-0lax">Section 1: Introduction <a href="./slides/" target='_blank'>[Slides]</a></td>
<td class="tg-0lax">Ekaterina</td>
</tr>
<tr>
<td class="tg-0lax">9:15—10:00</td>
<td class="tg-0lax">Section 2: LLMs for Writing Assistance <a href="./slides/" target='_blank'>[Slides]</a></td>
<td class="tg-0lax">Bashar</td>
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<td class="tg-0lax">10:00-10:45</td>
<td class="tg-0lax">Section 3: LLMs for Reading Assistance <a href="./slides/" target='_blank'>[Slides]</a></td>
<td class="tg-0lax">Sowmya</td>
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<td class="tg-0lax">10:45—11:15</td>
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<td class="tg-0lax">11:15—12:00</td>
<td class="tg-0lax">Section 4: LLMs for Spoken Language Learning and
Assessment <a href="./slides/" target='_blank'>[Slides]</a></td>
<td class="tg-0lax">Stefano</td>
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<td class="tg-0lax">12:00—12:45</td>
<td class="tg-0lax">Section 5: LLMs in Intelligent Tutoring Systems (ITS) <a href="./slides/" target='_blank'>[Slides]</a></td>
<td class="tg-0lax">Kaushal & Ekaterina</td>
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<td class="tg-0lax">12:45—1:00</td>
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<h2 class="title is-3">Reading List</h2>
<p>We've compiled a comprehensive reading list for each topic covered in this tutorial. Each topic has a dedicated reading list page along with the list of papers we'll discuss during the tutorial.
We welcome additions and suggestions from community for each topic. If you think that we're missing essential papers, please submit a <a href="https://github.com/coling2025-edu-llms/coling2025-edu-llms.github.io" target="_blank"> pull request.</a></p>
<br />
<h3 class="title is-5"><a href="./reading_lists/overview.html" target="_blank">Section 1: Overview</a></h3>
<ul>
<li><a href="https://link.springer.com/chapter/10.1007/978-3-642-00382-0_2">Opportunities for natural language processing research in education </a>(Burstein, 2009)</li>
<li> <a href="https://bera-journals.onlinelibrary.wiley.com/doi/full/10.1111/bjet.13370">Practical and ethical challenges of large language models in education: A systematic scoping review
</a> (Yan et.al., 2023)</li>
</ul>
<br />
<h3 class="title is-5"><a href="./reading_lists/writing_assistants.html" target="_blank">Section 2: LLMs for Writing Assistance</a></h3>
<ul>
<li> <a href="https://direct.mit.edu/coli/article/49/3/643/115846/Grammatical-Error-Correction-A-Survey-of-the-State">Grammatical Error Correction: A Survey of the State of the Art</a> (Bryant et al., 2023).</li>
<li> <a href="https://aclanthology.org/2023.bea-1.18">Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods</a> (Loem et al., 2023)</li>
<li> <a href="https://arxiv.org/abs/2303.14342">Analyzing the Performance of GPT-3.5 and GPT-4
in Grammatical Error Correction</a> (Coyne et al., 2023)</li>
<li><a href="https://aclanthology.org/2024.lrec-main.692">GPT-3.5 for Grammatical Error Correction</a> (Katinskaia et al., 2024)</li>
<li> <a href="https://aclanthology.org/2024.findings-acl.711"> Prompting open-source and commercial language models for grammatical error correction of English learner text</a> (Davis et al., 2024)</li>
<li> <a href="https://aclanthology.org/2024.bea-1.3">Pillars of Grammatical Error Correction: Comprehensive Inspection Of
Contemporary Approaches In The Era of Large Language Models</a> (Omelianchuk et al., 2024) </li>
<li> <a href="https://aclanthology.org/2024.lrec-main.350/">Controlled Generation with Prompt Insertion for Natural Language
Explanations in Grammatical Error Correction</a> (Kaneko et al., 2024)</li>
<li> <a href="https://aclanthology.org/2024.bea-1.6">Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction</a> (Kobayashi et al., 2024)</li>
<li> <a href="https://aclanthology.org/2023.findings-emnlp.689">Unraveling Downstream Gender Bias from Large Language Models: A
Study on AI Educational Writing Assistance</a> (Wambsganss et al., 2023)</li>
</ul>
<br />
<h3 class="title is-5"><a href="./reading_lists/reading_assistants.html" target="_blank">Section 3: LLMs for Reading Assistance</a></h3>
<ul style='margin-top:0cm' start=1 type=1>
<li> <a href="https://aclanthology.org/2023.emnlp-main.821/">BLESS: Benchmarking Large Language Models on
Sentence Simplification</a> (Kew et.al., 2023)</li>
<li><a href="https://link.springer.com/article/10.1007/s40593-023-00374-x">Review on Neural Question Generation for
Education Purposes</a> (Al Farabi et.al., 2023)</li>
<li><a href="http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.574.pdf">Trends,
limitations and open challenges in automatic readability assessment
research</a> (Vajjala, 2022)</li>
<li><a href="https://aclanthology.org/2021.findings-acl.233.pdf">Automatic text
simplification for social good: Progress and challenges</a> (Stajner, 2021)</li>
<li><a href="https://aclanthology.org/Q15-1021/">Problems
in Current Text Simplification Research: New Data Can Help</a> (Xu et.al., 2015)</li>
</ul><br />
<h3 class="title is-5"><a href="./reading_lists/spoken_language_assessment.html" target="_blank">Section 4: LLMs for Spoken Language Learning and Assessment</a></h3>
<ul style='margin-top:0cm' start=1 type=1>
<li> <a href="https://openreview.net/forum?id=14rn7HpKVk">SALMONN: Towards generic hearing abilities for large language models</a> (Tang et al., 2024)</li>
<li> <a href="https://aclanthology.org/2024.bea-1.14/">Can GPT-4 do L2 analytic assessment?</a> (Bannò et al., 2024)</li>
<li><a href="https://www.isca-archive.org/interspeech_2022/kim22k_interspeech.html">Automatic Pronunciation Assessment
using Self-Supervised Speech Representation Learning</a> (Kim et al., 2022)</li>
<li> <a href="https://www.isca-archive.org/interspeech_2021/peng21e_interspeech.html">A Study on Fine-Tuning wav2vec2.0 Model for the Task of
Mispronunciation Detection and Diagnosis.</a> (Peng et al., 2021)</li>
<li> <a href="https://aclanthology.org/P17-2008.pdf">Incorporating uncertainty into deep learning for spoken language assessment</a> (Malinin et al., 2017)</li>
<li><a href="https://books.google.ae/books?hl=en&lr=&id=88bADwAAQBAJ&oi=fnd&pg=PP1&dq=Automated+speaking+assessment:+Using+language+technologies+to+score+spontaneous+speech.&ots=jHm201a2oi&sig=VdGxpR5bTetVnXk_xc6p6VAcgSU&redir_esc=y#v=onepage&q=Automated%20speaking%20assessment%3A%20Using%20language%20technologies%20to%20score%20spontaneous%20speech.&f=false">Automated speaking assessment:
Using language technologies to score spontaneous speech.</a> (Zechner and Evanini, 2019)</li>
<li><a href="https://www.sciencedirect.com/science/article/abs/pii/S0346251X00000336?via%3Dihub">The ‘communicative’ legacy in
language testing</a> (Fulcher, 2000)</li>
</ul>
<br />
<h3 class="title is-5"><a href="./reading_lists/education_and_its.html" target="_blank">Section 5: LLMs in Intelligent Tutoring Systems (ITS)</a></h3>
<ul style='margin-top:0cm' start=1 type=1>
<li> <a href="https://arxiv.org/pdf/2305.14536">MATHDIAL: A Dialogue Tutoring Dataset with Rich Pedagogical
Properties Grounded in Math Reasoning Problems</a> (Macina et al., 2023)</li>
<li> <a href="https://arxiv.org/pdf/2306.03090">Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and
Providing Actionable Insights on Classroom Instruction</a> (Wang and Demszky, 2023)</li>
<li> <a href="https://arxiv.org/pdf/2205.07540">The AI Teacher Test: Measuring the Pedagogical Ability of
Blender and GPT-3 in Educational Dialogues</a> (Tack and Piech, 2022)</li>
<li><a href="https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2021.654924/full">Are We There Yet? -
A Systematic Literature Review on Chatbots in Education</a> (Wollny et al., 2021)</li>
<li> <a href="https://link.springer.com/content/pdf/10.1186/s41235-017-0087-y.pdf"> Teaching the science of learning</a> (Weinstein et al., 2018)</li>
<li> <a href="https://link.springer.com/article/10.1007/s40593-014-0029-5">AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring</a> (Nye et al., 2014)</li>
<li><a href="https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/download/1591/1490">Intelligent
tutoring systems with conversational dialogue</a> (Graesser et al., 2001)</li>
<li><a href="https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=ce9ccc58eb7db2c1ebc474ecba5772176afa71d3">The 2 sigma problem:
The search for methods of group instruction as effective as one-to-one tutoring</a> (Bloom, 1984)</li>
</ul>
<br />
<h3 class="title is-5"><a href="./reading_lists/challenges.html" target="_blank">Section 6: Challenges & Opportunities</a></h3>
We will finish the tutorial by discussing open challenges and opportunities as well as most promising future directions.
We solicit suggestions from the wider community on the material to cover in this section.
If you would like to suggest papers that we should cover, please submit a <a href="https://github.com/coling2025-edu-llms/coling2025-edu-llms.github.io" target="_blank"> pull request.
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