How to Harness Generative AI to Accelerate Human Learning

Johnson, W. Lewis

doi:10.1007/s40593-023-00367-w

Cited by 17 publications

(8 citation statements)

References 4 publications

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“…On the one hand, the ability of GenAI to provide explanations for its output is often criticized for the risk of communicating falsities with high confidence (e.g., in education; Johnson, 2023). On the other hand, natural conversations allow users to collaborate with the chatbot on solutions to yet unsolved problems.…”

Section: Advice Taking From Algorithms In the Age Of Conversational G...mentioning

confidence: 99%

Conversational User Interfaces: Explanations and Interactivity Positively Influence Advice Taking from Generative Artificial Intelligence

Rebholz,

Koop,

Hütter

2024

Preprint

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OBJECTIVE: Generative artificial intelligence (GenAI) has surged in popularity with the implementation of interactive conversational interfaces. The possibility to engage in natural conversations with GenAI thus constitutes a promising countermeasure to algorithm aversion, or the general preference for advice from humans over advice from algorithms, which hinders the effective utilization of often superior algorithmic output. We experimentally test the effectiveness of interactive conversational interfaces in enhancing advice taking from GenAIs. METHOD: In a judge-advisor system, 472 participants (313 female, 154 male, 5 diverse; median age 23 years) solved a series of Fermi problems with access to pre-generated output from OpenAI’s ChatGPT. In the control condition, only the numerical output was provided as advice. Participants in the treatment conditions were additionally provided with, or could request, a detailed explanation of the rationale underlying ChatGPT’s judgments. RESULTS: Advice taking was positively influenced by both the opportunity to interact with the GenAI and the receipt of an explanation. Moreover, actively requesting an explanation significantly enhanced the positive effect of interactivity compared to trials on which this opportunity was forgone. However, there was no evidence that the weighting of algorithmic advice was influenced by whether an explanation was provided or requested. CONCLUSION: The inherent explanatory capabilities of GenAI and the opportunity to interactively engage with it independently enhance users’ advice taking. This finding underscores the potential of interactive conversational interfaces to large language models such as ChatGPT to improve individuals’ augmented judgment and decision-making, but also poses a threat to human autonomy in interactions with conversational GenAI.

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Section: Advice Taking From Algorithms In the Age Of Conversational G...mentioning

confidence: 99%

Conversational User Interfaces: Explanations and Interactivity Positively Influence Advice Taking from Generative Artificial Intelligence

Rebholz,

Koop,

Hütter

2024

Preprint

View full text Add to dashboard Cite

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“…There has been an explosion of uses and proposed uses of GAIs in education, both by students and by teachers, performing all these past roles and more [5,52]. For teachers, GAIs can augment and replace their roles as Socratic tutors, providers of meaningful feedback, participants in discussions, and curriculum guides [53,54]. For students they can write assignments, perform research, summarize documents, and correct improper use of language [55].…”

Section: Gais and Educationmentioning

confidence: 99%

The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education

Dron

2023

Digital

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This paper analyzes the ways that the widespread use of generative AIs (GAIs) in education and, more broadly, in contributing to and reflecting the collective intelligence of our species, can and will change us. Methodologically, the paper applies a theoretical model and grounded argument to present a case that GAIs are different in kind from all previous technologies. The model extends Brian Arthur’s insights into the nature of technologies as the orchestration of phenomena to our use by explaining the nature of humans’ participation in their enactment, whether as part of the orchestration (hard technique, where our roles must be performed correctly) or as orchestrators of phenomena (soft technique, performed creatively or idiosyncratically). Education may be seen as a technological process for developing these soft and hard techniques in humans to participate in the technologies, and thus the collective intelligence, of our cultures. Unlike all earlier technologies, by embodying that collective intelligence themselves, GAIs can closely emulate and implement not only the hard technique but also the soft that, until now, was humanity’s sole domain; the very things that technologies enabled us to do can now be done by the technologies themselves. Because they replace things that learners have to do in order to learn and that teachers must do in order to teach, the consequences for what, how, and even whether learning occurs are profound. The paper explores some of these consequences and concludes with theoretically informed approaches that may help us to avert some dangers while benefiting from the strengths of generative AIs. Its distinctive contributions include a novel means of understanding the distinctive differences between GAIs and all other technologies, a characterization of the nature of generative AIs as collectives (forms of collective intelligence), reasons to avoid the use of GAIs to replace teachers, and a theoretically grounded framework to guide adoption of generative AIs in education.

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Section: Gais and Educationmentioning

confidence: 99%

The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education

Dron

2023

Preprint

View full text Add to dashboard Cite

This paper applies a theoretical model to analyze the ways that widespread use of generative AIs (GAIs) in education and, more broadly, in contributing to and reflecting the collective intelligence of our species, can and will change us. The model extends Brian Arthur’s insights into the nature of technologies as the orchestration of phenomena to our use by explaining the nature of humans participation in their enactment, whether as part of the orchestration (hard technique, where our roles must be performed correctly) or as orchestrators of phenomena (soft technique performed creatively or idiosyncratically). Education may be seen as a technological process for developing the soft and hard techniques of humans to participate in the technologies and thus the collective intelligence of our cultures. Unlike all earlier technologies, by embodying that collective intelli-gence themselves, GAIs can closely emulate and implement not only the hard technique but also the soft that, until now, was humanity’s sole domain: the very things that technologies enabled us to do can now be done by the technologies themselves. The consequences for what, how, and even whether we learn are profound. The paper explores some of these consequences and concludes with theoretically informed approaches that may help us to avert some dangers while benefiting from the strengths of generative AIs.

show abstract

How to Harness Generative AI to Accelerate Human Learning

Cited by 17 publications

References 4 publications

Conversational User Interfaces: Explanations and Interactivity Positively Influence Advice Taking from Generative Artificial Intelligence

Conversational User Interfaces: Explanations and Interactivity Positively Influence Advice Taking from Generative Artificial Intelligence

The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education

The Human Nature of Generative AIs and the Technological Nature of Humanity: Implications for Education

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