How Good is ChatGPT in Giving Adaptive Guidance Using Knowledge Graphs in E-Learning Environments?
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910728240234496 |
|---|---|
| author | Ocheja, Patrick Flanagan, Brendan Dai, Yiling Ogata, Hiroaki |
| author_facet | Ocheja, Patrick Flanagan, Brendan Dai, Yiling Ogata, Hiroaki |
| contents | E-learning environments are increasingly harnessing large language models (LLMs) like GPT-3.5 and GPT-4 for tailored educational support. This study introduces an approach that integrates dynamic knowledge graphs with LLMs to offer nuanced student assistance. By evaluating past and ongoing student interactions, the system identifies and appends the most salient learning context to prompts directed at the LLM. Central to this method is the knowledge graph's role in assessing a student's comprehension of topic prerequisites. Depending on the categorized understanding (good, average, or poor), the LLM adjusts its guidance, offering advanced assistance, foundational reviews, or in-depth prerequisite explanations, respectively. Preliminary findings suggest students could benefit from this tiered support, achieving enhanced comprehension and improved task outcomes. However, several issues related to potential errors arising from LLMs were identified, which can potentially mislead students. This highlights the need for human intervention to mitigate these risks. This research aims to advance AI-driven personalized learning while acknowledging the limitations and potential pitfalls, thus guiding future research in technology and data-driven education. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03856 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How Good is ChatGPT in Giving Adaptive Guidance Using Knowledge Graphs in E-Learning Environments? Ocheja, Patrick Flanagan, Brendan Dai, Yiling Ogata, Hiroaki Artificial Intelligence Emerging Technologies I.2.6; I.2.11 E-learning environments are increasingly harnessing large language models (LLMs) like GPT-3.5 and GPT-4 for tailored educational support. This study introduces an approach that integrates dynamic knowledge graphs with LLMs to offer nuanced student assistance. By evaluating past and ongoing student interactions, the system identifies and appends the most salient learning context to prompts directed at the LLM. Central to this method is the knowledge graph's role in assessing a student's comprehension of topic prerequisites. Depending on the categorized understanding (good, average, or poor), the LLM adjusts its guidance, offering advanced assistance, foundational reviews, or in-depth prerequisite explanations, respectively. Preliminary findings suggest students could benefit from this tiered support, achieving enhanced comprehension and improved task outcomes. However, several issues related to potential errors arising from LLMs were identified, which can potentially mislead students. This highlights the need for human intervention to mitigate these risks. This research aims to advance AI-driven personalized learning while acknowledging the limitations and potential pitfalls, thus guiding future research in technology and data-driven education. |
| title | How Good is ChatGPT in Giving Adaptive Guidance Using Knowledge Graphs in E-Learning Environments? |
| topic | Artificial Intelligence Emerging Technologies I.2.6; I.2.11 |
| url | https://arxiv.org/abs/2412.03856 |