The Bias–Variance Tradeoff in Cognitive Science

Doroudi, Shayan; Rastegar, Seyed Ali

doi:10.1111/cogs.13241

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…More recently, the field of machine teaching is interested in how insights from how to optimally teach machine learners may apply to teaching human learners [32]. Recent work has also shown how the bias-variance tradeoff in machine learning may help explain recent results on individual differences in how people learn [10] as well as debates around what instructional strategies are optimal [8].…”

Section: Role Of Ai Axismentioning

confidence: 99%

Four Interactions Between AI and Education: Broadening Our Perspective on What AI Can Offer Education

Rismanchian¹,

Doroudi²

2023

Preprint

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In the 30th anniversary of the International Artificial Intelligence in Education Society, there is a need to look back to the past to envision the community's future. This paper presents a new framework (AIxEd) to categorize different interactions between artificial intelligence (AI) and education. We use our framework to compare papers from two early proceedings of AIED (1985 \& 1993) with AIED proceedings papers and IJAIED papers published in 2021. We find that two out of four kinds of interactions between AI and education were more common in the early stages of the field but have gained less attention from the community in recent years. We suggest that AI has more to offer education than a pragmatic toolkit to apply to educational problems; rather, AI also serves as an analogy for thinking about human intelligence and learning. We conclude by envisioning future research directions in AIED from the lens of our framework.

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Section: Role Of Ai Axismentioning

confidence: 99%

Four Interactions Between AI and Education: Broadening Our Perspective on What AI Can Offer Education

Rismanchian¹,

Doroudi²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…More recently, the field of machine teaching is interested in how insights from how to optimally teach machine learners may apply to teaching human learners [33]. Recent work has also shown how the bias-variance tradeoff in machine learning may help explain recent results on individual differences in how people learn [10] as well as debates around what instructional strategies are optimal [8].…”

Section: Role Of Ai Axismentioning

confidence: 99%

Four Interactions Between AI and Education: Broadening Our Perspective on What AI Can Offer Education

Rismanchian

Doroudi

2023

Communications in Computer and Information Science

Self Cite

View full text Add to dashboard Cite

In the 30th anniversary of the International Artificial Intelligence in Education Society, there is a need to look back to the past to envision the community's future. This paper presents a new framework (AI×Ed) to categorize different interactions between artificial intelligence (AI) and education. We use our framework to compare papers from two early proceedings of AIED (1985AIED ( & 1993 with AIED proceedings papers and IJAIED papers published in 2021. We find that two out of four kinds of interactions between AI and education were more common in the early stages of the field but have gained less attention from the community in recent years. We suggest that AI has more to offer education than a pragmatic toolkit to apply to educational problems; rather, AI also serves as an analogy for thinking about human intelligence and learning. We conclude by envisioning future research directions in AIED.

show abstract