| Title: |
Structured Human-LLM Interaction Design Reveals Exploration and Exploitation Dynamics in Higher Education Content Generation |
| Language: |
English |
| Authors: |
Pablo Flores Romero (ORCID 0009-0001-9008-5356); Kin Nok Nicholas Fung; Guang Rong; Benjamin Ultan Cowley |
| Source: |
npj Science of Learning. 2025 10. |
| Availability: |
Nature Portfolio. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://www.nature.com/npjscilearn/ |
| Peer Reviewed: |
Y |
| Page Count: |
13 |
| Publication Date: |
2025 |
| Document Type: |
Journal Articles; Reports - Research |
| Education Level: |
Higher Education; Postsecondary Education |
| Descriptors: |
Man Machine Systems; Artificial Intelligence; Natural Language Processing; Interaction; Design; Higher Education; Doctoral Students; Technology Uses in Education; Computation; Thinking Skills; Discovery Learning; Prompting; Cues; Cooperative Learning |
| DOI: |
10.1038/s41539-025-00332-3 |
| ISSN: |
2056-7936 |
| Abstract: |
Large Language Models (LLMs) present a radically new paradigm for the study of "information foraging behavior." We study how LLM technology is used for pedagogical content creation by a sample of 25 participants in a doctoral-level Artificial Intelligence (AI) in Education course, and the role of computational-thinking skills in shaping their foraging behavior. We used editable prompt templates and socially-sourced keywords to structure their prompt-crafting process. This design influenced participants' behaviors towards "exploration" (to generate novel information landscapes) and "exploitation" (to dive into specific content). Findings suggest that exploration facilitates navigation of semantically diverse information, especially when influenced by social cues. In contrast, exploitation narrows the focus to using AI-generated content. Participants also completed a Computational Thinking survey: exploratory analyses suggest that trait cooperativity encourages exploitation of AI content, while trait critical thinking moderates reliance on participants' own interests. We discuss implications for future use of LLM-driven educational tools. |
| Abstractor: |
As Provided |
| Entry Date: |
2025 |
| Accession Number: |
EJ1475018 |
| Database: |
ERIC |