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As Artificial Intelligence (AI) becomes ubiquitous in children’s lives, it is crucial to introduce newand effective ways to teach the younger generation about AI. This study describes the experience of training undergraduate students as middle school AI summer camp facilitators. These undergraduates participated in a professional development session prior to camp for 9 days (54 hours), and then facilitated AI learning and camp activities within the two-week camp. Our findings indicate that the undergraduate facilitators benefited greatly from this experience, in turn providing benefits to the middle school learners. The undergraduates developed both professional and conceptual skills. By interacting with the facilitators as “near-peers”, the students learned about AI in a fun, engaging, and supportive way. These findings contribute to our understanding of how to effectively teach young learners about complex AI concepts.
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Artificial Conscious Intelligence
The field of artificial consciousness (AC) has largely developed outside of mainstream artificial intelligence (AI), with separate goals and criteria for success and with only a minimal exchange of ideas. This is unfortunate as the two fields appear to be synergistic. For example, here we consider the question of how concepts developed in AC research might contribute to more effective future AI systems. We first briefly discuss several past hypotheses about the function(s) of human consciousness, and present our own hypothesis that short-term working memory and very rapid learning should be a central concern in such matters. In this context, we then present ideas about how integrating concepts from AC into AI systems to develop an artificial conscious intelligence (ACI) could both produce more effective AI technology and contribute to a deeper scientific understanding of the fundamental nature of consciousness and intelligence.
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- Award ID(s):
- 1632976
- PAR ID:
- 10187194
- Date Published:
- Journal Name:
- Journal of Artificial Intelligence and Consciousness
- Volume:
- 07
- Issue:
- 01
- ISSN:
- 2705-0785
- Page Range / eLocation ID:
- 95 to 107
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1142/S270507852050006X
