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Comparing Knowledge Injection in LLMs", "creators": [ { "creatorType": "author", "firstName": "Oded", "lastName": "Ovadia" }, { "creatorType": "author", "firstName": "Menachem", "lastName": "Brief" }, { "creatorType": "author", "firstName": "Moshik", "lastName": "Mishaeli" }, { "creatorType": "author", "firstName": "Oren", "lastName": "Elisha" } ], "abstractNote": "Large language models (LLMs) encapsulate a vast amount of factual information within their pre-trained weights, as evidenced by their ability to answer diverse questions across different domains. However, this knowledge is inherently limited, relying heavily on the characteristics of the training data. Consequently, using external datasets to incorporate new information or refine the capabilities of LLMs on previously seen information poses a significant challenge. In this study, we compare two common approaches: unsupervised fine-tuning and retrieval-augmented generation (RAG). 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Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), promise powerful tools and capabilities to enhance personalized learning in online educational environments. In this work, we explore how LLMs can improve personalized learning experiences by catering to individual user needs toward enhancing the overall quality of online education. We designed personalization guidelines based on the growing literature on personalized learning to ground LLMs in generating tailored learning plans. To operationalize these guidelines, we implemented LearnMate, an LLM-based system that generates personalized learning plans and provides users with real-time learning support. We discuss the implications and future directions of this work, aiming to move beyond the traditional one-size-fits-all approach by integrating LLM-based personalized support into online learning environments.", "genre": "", "repository": "", "archiveID": "", "place": "", "date": "2025-03-17", "series": "", "seriesNumber": "", "DOI": "10.1145/3706599.3719857", "citationKey": "", "url": "http://arxiv.org/abs/2503.13340", "accessDate": "2025-03-20T19:27:17Z", "archive": "", "archiveLocation": "", "shortTitle": "LearnMate", "language": "", "libraryCatalog": "arXiv.org", "callNumber": "", "rights": "", "extra": "arXiv:2503.13340 [cs]", "tags": [ { "tag": "Computer Science - Human-Computer Interaction", "type": 1 } ], "collections": [ "DTG8HR8P" ], "relations": {}, "dateAdded": "2025-03-20T19:27:17Z", "dateModified": "2025-03-20T19:27:17Z" } }, { "key": "DMX7DP5X", "version": 8490, "library": { "type": "user", "id": 36278, "name": "David Wiley", "links": { "alternate": { "href": "https://www.zotero.org/opencontent", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/users/36278/items/DMX7DP5X", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/opencontent/items/DMX7DP5X", "type": "text/html" }, "up": { "href": "https://api.zotero.org/users/36278/items/IWRSEFKF", "type": "application/json" } }, "meta": { "numChildren": false }, "data": { "key": "DMX7DP5X", "version": 8490, "parentItem": "IWRSEFKF", "itemType": "attachment", "linkMode": "imported_file", "title": "PDF", "accessDate": "", "url": "", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Li et al. - 2025 - Can AI support human grading Examining machine attention and confidence in short answer scoring.pdf", "md5": "ecadbbd764698ad2170687209ad6465a", "mtime": 1738591736859, "tags": [], "relations": {}, "dateAdded": "2025-02-03T14:08:56Z", "dateModified": "2025-02-03T14:08:56Z" } }, { "key": "4NSY9IRA", "version": 8486, "library": { "type": "user", "id": 36278, "name": "David Wiley", "links": { "alternate": { "href": "https://www.zotero.org/opencontent", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/users/36278/items/4NSY9IRA", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/opencontent/items/4NSY9IRA", "type": "text/html" }, "up": { "href": "https://api.zotero.org/users/36278/items/IWRSEFKF", "type": "application/json" } }, "meta": {}, "data": { "key": "4NSY9IRA", "version": 8486, "parentItem": "IWRSEFKF", "itemType": "attachment", "linkMode": "imported_url", "title": "ScienceDirect Snapshot", "accessDate": "2025-02-03T14:08:05Z", "url": "https://www.sciencedirect.com/science/article/pii/S0360131525000120?via%3Dihub", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "S0360131525000120.html", "md5": "d820c879a797bd15a7d61bc7becb3084", "mtime": 1738591685190, "tags": [], "relations": {}, "dateAdded": "2025-02-03T14:08:05Z", "dateModified": "2025-02-03T14:08:05Z" } }, { "key": "IWRSEFKF", "version": 8484, "library": { "type": "user", "id": 36278, "name": "David Wiley", "links": { "alternate": { "href": "https://www.zotero.org/opencontent", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/users/36278/items/IWRSEFKF", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/opencontent/items/IWRSEFKF", "type": "text/html" } }, "meta": { "creatorSummary": "Li et al.", "parsedDate": "2025-04-01", "numChildren": 2 }, "data": { "key": "IWRSEFKF", "version": 8484, "itemType": "journalArticle", "title": "Can AI support human grading? Examining machine attention and confidence in short answer scoring", "creators": [ { "creatorType": "author", "firstName": "Yuheng", "lastName": "Li" }, { "creatorType": "author", "firstName": "Mladen", "lastName": "Raković" }, { "creatorType": "author", "firstName": "Namrata", "lastName": "Srivastava" }, { "creatorType": "author", "firstName": "Xinyu", "lastName": "Li" }, { "creatorType": "author", "firstName": "Quanlong", "lastName": "Guan" }, { "creatorType": "author", "firstName": "Dragan", "lastName": "Gašević" }, { "creatorType": "author", "firstName": "Guanliang", "lastName": "Chen" } ], "abstractNote": "Large language models built upon artificial intelligence (AI) hold great promises to innovate automatic short answer scoring (ASAS) - significantly alleviating educators’ workload in assessing student answers. However, ASAS systems on such basis have seen limited adoption in authentic teaching environments due to the models’ inability to explain the predictions they generate. To address this, we recruited 32 human graders to comparatively analyse the decision-making processes of human graders and AI-driven graders. Specifically, we exploited two types of data to holistically unveil the decision-making processes of human graders throughout grading, namely manual annotation of important words and gaze data of the human graders. The decision-making processes of AI-driven graders were revealed by important words extracted though eXplainable Artificial Intelligence technique and grading confidence reflected by the prediction probability distributions. We measured the alignment in their decision-making regarding their (i) estimated scoring difficulty, (ii) important text segments and (iii) crucial grammatical categories to enhance the transparency and trustworthiness of AI-driven graders. Subsequently, we conducted randomised control studies, presenting machine-extracted insights like important words and estimated scoring difficulty to scrutinise how they affected human grading. Our findings contribute new knowledge regarding the consistency between human and machine scoring and validates machine-extracted insights, such as important words and scoring difficulty, to be valuable in facilitating human grading, encouraging the adoption of ASAS systems and urging the potential collaboration between machine and human grading in pedagogical practices. However, we emphasised the significance of grasping question context and intricacy before leveraging such machine-extracted insights.", "publicationTitle": "Computers & Education", "publisher": "", "place": "", "date": "2025-04-01", "volume": "228", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "105244", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Computers & Education", "DOI": "10.1016/j.compedu.2025.105244", "citationKey": "", "url": "https://www.sciencedirect.com/science/article/pii/S0360131525000120", "accessDate": "2025-02-03T14:07:58Z", "PMID": "", "PMCID": "", "ISSN": "0360-1315", "archive": "", "archiveLocation": "", "shortTitle": "Can AI support human grading?", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Automatic short answer scoring", "type": 1 }, { "tag": "Eye-tracking", "type": 1 }, { "tag": "Human–AI collaboration", "type": 1 }, { "tag": "Neural networks", "type": 1 }, { "tag": "Trustworthiness", "type": 1 }, { "tag": "XAI in education", "type": 1 } ], "collections": [ "DTG8HR8P" ], "relations": {}, "dateAdded": "2025-02-03T14:07:58Z", "dateModified": "2025-02-03T14:07:58Z" } }, { "key": "Z7KB673G", "version": 8463, "library": { "type": "user", "id": 36278, "name": "David Wiley", "links": { "alternate": { "href": "https://www.zotero.org/opencontent", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/users/36278/items/Z7KB673G", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/opencontent/items/Z7KB673G", "type": "text/html" }, "up": { "href": "https://api.zotero.org/users/36278/items/4WPXPBH9", "type": "application/json" } }, "meta": {}, "data": { "key": "Z7KB673G", "version": 8463, "parentItem": "4WPXPBH9", "itemType": "attachment", "linkMode": "imported_url", "title": "Snapshot", "accessDate": "2025-01-14T14:12:33Z", "url": "https://arxiv.org/abs/2501.06682", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "2501.html", "md5": "235b5d7ec1bbd9c827027252b68a1db6", "mtime": 1736863953627, "tags": [], "relations": {}, "dateAdded": "2025-01-14T14:12:33Z", "dateModified": "2025-01-14T14:12:33Z" } }, { "key": "DRB8CWA2", "version": 8464, "library": { "type": "user", "id": 36278, "name": "David Wiley", "links": { "alternate": { "href": "https://www.zotero.org/opencontent", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/users/36278/items/DRB8CWA2", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/opencontent/items/DRB8CWA2", "type": "text/html" }, "up": { "href": "https://api.zotero.org/users/36278/items/4WPXPBH9", "type": "application/json" } }, "meta": { "numChildren": false }, "data": { "key": "DRB8CWA2", "version": 8464, "parentItem": "4WPXPBH9", "itemType": "attachment", "linkMode": "imported_url", "title": "Preprint PDF", "accessDate": "2025-01-14T14:12:29Z", "url": "http://arxiv.org/pdf/2501.06682v1", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Hu et al. - 2025 - Generative AI in Education From Foundational Insights to the Socratic Playground for Learning.pdf", "md5": "17df6043156dbf09699e0c62da1cfbec", "mtime": 1736863949160, "tags": [], "relations": {}, "dateAdded": "2025-01-14T14:12:29Z", "dateModified": "2025-01-14T14:12:29Z" } }, { "key": "4WPXPBH9", "version": 8461, "library": { "type": "user", "id": 36278, "name": "David Wiley", "links": { "alternate": { "href": "https://www.zotero.org/opencontent", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/users/36278/items/4WPXPBH9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/opencontent/items/4WPXPBH9", "type": "text/html" } }, "meta": { "creatorSummary": "Hu et al.", "parsedDate": "2025-01-12", "numChildren": 2 }, "data": { "key": "4WPXPBH9", "version": 8461, "itemType": "preprint", "title": "Generative AI in Education: From Foundational Insights to the Socratic Playground for Learning", "creators": [ { "creatorType": "author", "firstName": "Xiangen", "lastName": "Hu" }, { "creatorType": "author", "firstName": "Sheng", "lastName": "Xu" }, { "creatorType": "author", "firstName": "Richard", "lastName": "Tong" }, { "creatorType": "author", "firstName": "Art", "lastName": "Graesser" } ], "abstractNote": "This paper explores the synergy between human cognition and Large Language Models (LLMs), highlighting how generative AI can drive personalized learning at scale. We discuss parallels between LLMs and human cognition, emphasizing both the promise and new perspectives on integrating AI systems into education. After examining challenges in aligning technology with pedagogy, we review AutoTutor-one of the earliest Intelligent Tutoring Systems (ITS)-and detail its successes, limitations, and unfulfilled aspirations. We then introduce the Socratic Playground, a next-generation ITS that uses advanced transformer-based models to overcome AutoTutor's constraints and provide personalized, adaptive tutoring. To illustrate its evolving capabilities, we present a JSON-based tutoring prompt that systematically guides learner reflection while tracking misconceptions. Throughout, we underscore the importance of placing pedagogy at the forefront, ensuring that technology's power is harnessed to enhance teaching and learning rather than overshadow it.", "genre": "", "repository": "arXiv", "archiveID": "arXiv:2501.06682", "place": "", "date": "2025-01-12", "series": "", "seriesNumber": "", "DOI": "10.48550/arXiv.2501.06682", "citationKey": "", "url": "http://arxiv.org/abs/2501.06682", "accessDate": "2025-01-14T14:12:28Z", "archive": "", "archiveLocation": "", "shortTitle": "Generative AI in Education", "language": "", "libraryCatalog": "arXiv.org", "callNumber": "", "rights": "", "extra": "arXiv:2501.06682 [cs]", "tags": [ { "tag": "Computer Science - Artificial Intelligence", "type": 1 } ], "collections": [ "DTG8HR8P" ], "relations": {}, "dateAdded": "2025-01-14T14:12:28Z", "dateModified": "2025-01-14T14:12:28Z" } }, { "key": "NQ6X8GBP", "version": 8460, "library": { "type": "user", "id": 36278, "name": "David Wiley", "links": { "alternate": { "href": "https://www.zotero.org/opencontent", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/users/36278/items/NQ6X8GBP", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/opencontent/items/NQ6X8GBP", "type": "text/html" }, "up": { "href": "https://api.zotero.org/users/36278/items/J76PNBNF", "type": "application/json" } }, "meta": {}, "data": { "key": "NQ6X8GBP", "version": 8460, "parentItem": "J76PNBNF", "itemType": "annotation", "annotationType": "highlight", "annotationText": "the impact of LLMs on learning outcomes should be continuously evaluated to ensure their effectiveness and avoid unintended negative consequences.", "annotationComment": "", "annotationColor": "#ffd400", "annotationPageLabel": "9", "annotationSortIndex": "00008|003221|00526", "annotationPosition": "{\"pageIndex\":8,\"rects\":[[476.579,319.026,524.415,328.484],[306.142,305.477,526.217,314.935],[306.142,291.928,524.409,301.386],[306.142,278.379,485.619,287.837]]}", "tags": [], "relations": {}, "dateAdded": "2025-01-11T16:38:30Z", "dateModified": "2025-01-11T16:38:30Z" } }, { "key": "IP62CNXE", "version": 8459, "library": { "type": "user", "id": 36278, "name": "David Wiley", "links": { "alternate": { "href": "https://www.zotero.org/opencontent", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/users/36278/items/IP62CNXE", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/opencontent/items/IP62CNXE", "type": "text/html" }, "up": { "href": "https://api.zotero.org/users/36278/items/J76PNBNF", "type": "application/json" } }, "meta": {}, "data": { "key": "IP62CNXE", "version": 8459, "parentItem": "J76PNBNF", "itemType": "annotation", "annotationType": "highlight", "annotationText": "Notably, with optimal beta values, KTO outperforms both DPO and IPO across all models, emphasizing its effectiveness in pedagogical alignment tasks when properly tuned. Our findings underscore the necessity of careful hyperparameter tuning in LHP algorithms to achieve optimal pedagogical alignment. 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