Abstract
Holding the promise of higher learning outcomes, discovery learning utilizes intrinsic motivation to provide an enjoyable self-directed learning experience. Unfortunately, this approach can also lead to a sub-optimal cognitive load, which hinders learning. To avoid this, players must be in the optimal Zone of Proximal Development (ZPD). A way of accomplishing this is to make use of Artificial Intelligence in a narrative-centered discovery game using adaptive guidance. Textual instructions were automatically adapted in real-time to ensure a personalized challenge for one group of learners, where a control group received static instructions. Compared to the control group, the learners with personalized instructions showed higher story and spatial learning, while having decreased cognitive load and a similar learning experience. So, instructions given to self-directed learners can be personalized in real-time, which not only reduces learners’ cognitive load but also leads to enhanced learning outcomes without affecting the learning experience.
Original language | English |
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Article number | 100089 |
Number of pages | 9 |
Journal | Computers & Education: Artificial Intelligence |
Volume | 3 |
DOIs | |
Publication status | Published - 2022 |
Keywords
- Artificial intelligence in education (AIEd)
- discovery learning
- textual specificity
- Zone of proximal development (ZPD)
- Personalization
- Experience