Agri-Tourism Learning Hubs: A Cognitive Science-Driven Framework for Integrated Rural Revitalization via Spaced Repetition and AI Feedback
Abstract
AI-driven personalized guidance to advance rural development. The system combines practical training with mobile-enabled spaced repetition, dynamically adjusting review intervals to enhance skill retention. An AI transformer architecture tailors learning pathways through performance analytics, while augmented reality facilitates real-time workshop interaction. Crucially, it establishes a bidirectional farmer-tourist
knowledge exchange loop, where crowdsourced data refines adaptive learning. Our methodology uniquely synthesizes observational learning,
memory decay theory, and reinforcement learning into a closed-loop framework, simultaneously addressing educational and economic challenges. Implemented with scalable technologies (including GPT-4o and NVIDIA DGX A100), the system demonstrates real-time adaptability.
The core contribution lies in harmonizing agritourism with cognitive science, delivering a reproducible framework for sustainable rural development through AI-augmented participatory education and feedback mechanisms.
Keywords
Full Text:
PDFReferences
[1] W Pevetz (1991) Agriculture and tourism in Austria. Tourism Recreation Research.
[2] B Tabibian, U Upadhyay, A De, A Zarezade, et al. (2019) Enhancing human learning via spaced repetition optimization. In Proceedings
of the National Academy of Sciences.
[3] T Kabudi, I Pappas & DH Olsen (2021) AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and
Education: Artificial Intelligence.
[4] O Oluwaseun, F Tolulope, B Lukuman, et al. (2020) Improving pre-service teachers memory and retention using durable memory strategies in an instructional technology course. American Journal of Educational Research.
[5] JH Momsen (1998) Caribbean Tourism and Agriculture, Globalization and Neoliberalism: The Caribbean Context.
[6] JR Anderson & G Feder (2007) Agricultural extension. Handbook of agricultural economics.
[7] A Traspadini, M Giordani, et al. (2023) Real-time HAP-assisted vehicular edge computing for rural areas. IEEE Wireless Communications Letters.
[8] J Su, Y Yang & X Zhang (2019) Knowledge transfer efficiency measurement with application for open innovation networks. Interna-
tional Journal of Technology Management.
[9] M Nabiyouni, S Scerbo, DA Bowman & T Hllerer (2017) Relative effects of real-world and virtual-world latency on an augmented reality training task: an ar simulation experiment. Frontiers in ICT.
[10] L Liu, F Wu, H Tong, C Hao & T Xie (2021) The digital divide and active aging in China. International Journal of Environmental Research and Public Health.
[11] FA Gwelo (2025) Algorithmic Bias and Farmers Autonomy in AI-Driven Agricultural Marketing and Supply Chains. AI and Machine
Learning Applications in Supply Chain Management.
[12] P Zhang, Z Guo, S Ullah, G Melagraki, A Afantitis, et al. (2021) Nanotechnology and artificial intelligence to enable sustainable and
precision agriculture. Nature Plants.
[13] LA Gridin, YA Tsypkin & TA Kudryashova (2021) Using Blockchain Technologies in Solving Social and Economic Problems in Rural
Areas. The Challenge of Sustainability in the Digital Age.
DOI: http://dx.doi.org/10.70711/frim.v3i10.7530
Refbacks
- There are currently no refbacks.
