Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human-like learning

Oudeyer, Pierre-Yves

doi:10.1017/s0140525x17000243

Cited by 9 publications

(5 citation statements)

References 29 publications

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“…The finding that Montessori students missed fewer trials and had more incorrect trials could reflect the emphasis on exploratory learning in Montessori classrooms 62,63 . The extent of exploratory learning through trial-and-error is known to depend on the structure of the environment, the task complexity and the instructions given; these features together have been shown to impact self-directed executive functions and curiosity among children [64][65][66][67] . This explanation would also be consistent with the fact that, in our study, Montessori students' showed stronger neural activation during math processing in bilateral occipital and parietal cortices, involved in multisensory integration 68,69 , and in the right inferior parietal lobule, known to be recruited for math processing 70 .…”

Section: Discussionmentioning

confidence: 99%

An fMRI study of error monitoring in Montessori and traditionally-schooled children

et al. 2020

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The development of error monitoring is central to learning and academic achievement. However, few studies exist on the neural correlates of children's error monitoring, and no studies have examined its susceptibility to educational influences. Pedagogical methods differ on how they teach children to learn from errors. Here, 32 students (aged 8-12 years) from high-quality Swiss traditional or Montessori schools performed a math task with feedback during fMRI. Although the groups' accuracies were similar, Montessori students skipped fewer trials, responded faster and showed more neural activity in right parietal and frontal regions involved in math processing. While traditionally-schooled students showed greater functional connectivity between the ACC, involved in error monitoring, and hippocampus following correct trials, Montessori students showed greater functional connectivity between the ACC and frontal regions following incorrect trials. The findings suggest that pedagogical experience influences the development of error monitoring and its neural correlates, with implications for neurodevelopment and education.

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Section: Discussionmentioning

confidence: 99%

An fMRI study of error monitoring in Montessori and traditionally-schooled children

et al. 2020

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“…AI has been shown to increase classroom engagement in project-based learning settings [51,78] and stimulate learning motivation by providing immersive experiences through hologram technology [79]. Oudeyer (2017) and Popenici and Kerr (2017) emphasized the pivotal role of AI in fostering autonomous learning, curiosity, and intrinsic motivation, which are crucial elements for social learning and peer interactions [80,81]. The impact of AI in higher education has also been extensively researched [81], and the emerging field of AI in education (AIEd) is revolutionizing educational technologies [74,82].…”

Section: Research Hypothesismentioning

confidence: 99%

Towards a New Conceptual Model of AI-Enhanced Learning for College Students: The Roles of Artificial Intelligence Capabilities, General Self-Efficacy, Learning Motivation, and Critical Thinking Awareness

Jia,

2024

Systems

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In the aftermath of the COVID-19 pandemic, college students have faced various challenges that could negatively impact their critical thinking abilities due to disruptions to education, increased stress and anxiety, less social interaction, and the advancement of distance learning relying more heavily on digital tools. With the increasing integration of AI technology across sectors, higher education institutions have deployed various AI capabilities for intelligent campuses and modernized teaching. However, how to fully utilize AI capabilities to promote students’ thinking awareness on learning effectiveness is still not clear, as critical thinking is an essential skill set holding significant implications for college students’ development. This research adopts the resource-based theory (RBT) to conceptualize the university as a unified entity of artificial intelligence (AI) resources. It aims to investigate whether AI capabilities can foster critical thinking awareness among students by enhancing general self-efficacy and learning motivation. In particular, it examines the causal relationships between AI capabilities, general self-efficacy, motivation and critical thinking awareness. Primary data was collected through a questionnaire administered to 637 college students. Structural equation modeling was employed to test hypotheses pertaining to causality. The results showed that AI capabilities could indirectly enhance students’ critical thinking awareness by strengthening general self-efficacy and learning motivation, but the effect on critical thinking awareness was not significant. Meanwhile, general self-efficacy significantly affected the formation of learning motivation and critical thinking awareness. This indicates that AI capabilities are able to reshape the cognitive learning process, but its direct influence on thinking awareness needs to be viewed with caution. This study explored the role of AI capabilities in education from the perspective of organizational capabilities. It not only proves how AI facilitates cognition, but also discovered the important mediating role of general self-efficacy and motivation in this process. This finding explains the inherent connections between the mechanism links. Furthermore, the study expands research on AI capabilities research from the technical level to the educational field. It provides a comprehensive and in-depth theoretical explanation theoretically, guiding the practice and application of AI in education. The study is of positive significance for understanding the need for the future development of the cultivation of critical thinking awareness talents needed for future development through AI capabilities in education.

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“…Goal selection in artificial agents Enabling agents to learn autonomously has been a longstanding challenge in artificial intelligence and a central tenet of many successes in the field [64,65,43,[66][67][68]. However, most existing models are optimized for a predetermined objective.…”

Section: Related Workmentioning

confidence: 99%

Latent Learning Progress Drives Autonomous Goal Selection in Human Reinforcement Learning

Molinaro,

Colas,

Oudeyer

et al. 2024

Preprint

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Humans are autotelic agents who learn by setting and pursuing their own goals. However, the precise mechanisms guiding human goal selection remain unclear. Learning progress, typically measured as the observed change in performance, can provide a valuable signal for goal selection in both humans and artificial agents. We hypothesize that human choices of goals may also be driven by latent learning progress, which humans can estimate through knowledge of their actions and the environment – even without experiencing immediate changes in performance. To test this hypothesis, we designed a hierarchical reinforcement learning task in which human participants (N = 175) repeatedly chose their own goals and learned goal-conditioned policies. Our behavioral and computational modeling results confirm the influence of latent learning progress on goal selection and uncover inter-individual differences, partially mediated by recognition of the environment’s hierarchical structure. By investigating the role of latent learning progress in human goal selection, we pave the way for more effective and personalized learning experiences as well as the advancement of more human-like autotelic machines.

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Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human-like learning

Cited by 9 publications

References 29 publications

An fMRI study of error monitoring in Montessori and traditionally-schooled children

An fMRI study of error monitoring in Montessori and traditionally-schooled children

Towards a New Conceptual Model of AI-Enhanced Learning for College Students: The Roles of Artificial Intelligence Capabilities, General Self-Efficacy, Learning Motivation, and Critical Thinking Awareness

Latent Learning Progress Drives Autonomous Goal Selection in Human Reinforcement Learning

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