Pedagogically-Informed Implementation of Reinforcement Learning on Knowledge Graphs for Context-Aware Learning Recommendations

Abu-Rasheed, Hasan; Weber, Christian; Dornhöfer, Mareike; Fathi, Madjid

doi:10.1007/978-3-031-42682-7_35

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…1 https://en.wikipedia.org/wiki/Paras_Khadka To eliminate both these problems, researchers have integrated LLMs with contextual knowledge in the form of knowledge graphs [114] and contextual prompts [115], thereby giving LLMs useful prior information for detecting misinformation and disinformation. However, these methods are not without limitations as contextual knowledge databases may not be able to keep up with the ever-changing online landscape.…”

Section: Llms In Spreading Misinformation and Disinformationmentioning

confidence: 99%

Navigating the Web of Disinformation and Misinformation: Large Language Models as Double-Edged Swords

Shah,

Thapa,

Acharya

et al. 2024

IEEE Access

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This paper explores the dual role of Large Language Models (LLMs) in the context of online misinformation and disinformation. In today's digital landscape, where the internet and social media facilitate the rapid dissemination of information, discerning between accurate content and falsified information presents a formidable challenge. Misinformation, often arising unintentionally, and disinformation, crafted deliberately, are at the forefront of this challenge. LLMs such as OpenAI's GPT-4, equipped with advanced language generation abilities, present a double-edged sword in this scenario. While they hold promise in combating misinformation by fact-checking and detecting LLM-generated text, their ability to generate realistic, contextually relevant text also poses risks for creating and propagating misinformation. Further, LLMs are plagued with many problems such as biases, knowledge cutoffs, and hallucinations, which may further perpetuate misinformation and disinformation. The paper outlines historical developments in misinformation detection and how it affects social media consumption, especially among youth, and introduces LLMs and their applications in various domains. It then critically analyzes the potential of LLMs to generate and counter misinformation and disinformation in sensitive topics such as healthcare, COVID-19, and political agendas. Further, it discusses mitigation strategies, ethical considerations, and regulatory measures, summarizing previous methods and proposing future research direction toward leveraging the benefits of LLMs while minimizing misuse risks. The paper concludes by acknowledging LLMs as powerful tools with significant implications in both spreading and combating misinformation in the digital age.

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Section: Llms In Spreading Misinformation and Disinformationmentioning

confidence: 99%

Navigating the Web of Disinformation and Misinformation: Large Language Models as Double-Edged Swords

Shah,

Thapa,

Acharya

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The recommendation algorithm we use is a graph exploration and path weighing algorithm [18], based on Markov decision process (MDP) to identify an optimal learning path for each learner. Recommended learning path is explained in terms of its content selection (why each node is recommended), as well as its relation to the learning goal (how a node supports achieving the desired learning goal).…”

Section: Knowledge Graph Structurementioning

confidence: 99%

Knowledge Graphs as Context Sources for LLM-Based Explanations of Learning Recommendations

Abu-Rasheed,

Weber,

Fathi

2024

2024 IEEE Global Engineering Education Conference (EDUCON)

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TExKG in Health Domain: The Application of Knowledge Graph Based Framework for Explainable Recommendations in the Contexts of Elderly Care, Mental Health, and Emergency Responses

Abu-Rasheed,

Nadeem,

Dornhöfer

et al. 2024

Integrated Systems: Data Driven Engineering

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Pedagogically-Informed Implementation of Reinforcement Learning on Knowledge Graphs for Context-Aware Learning Recommendations

Cited by 4 publications

References 14 publications

Navigating the Web of Disinformation and Misinformation: Large Language Models as Double-Edged Swords

Navigating the Web of Disinformation and Misinformation: Large Language Models as Double-Edged Swords

Knowledge Graphs as Context Sources for LLM-Based Explanations of Learning Recommendations

TExKG in Health Domain: The Application of Knowledge Graph Based Framework for Explainable Recommendations in the Contexts of Elderly Care, Mental Health, and Emergency Responses

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