Optimize to Open: An Exploratory-Experimental Approach to the Computational Optimization of Open Large Language Models for Educational Access

Iván Miguel García-López; José-Martín Molina-Espinosa; María-Soledad Ramírez-Montoya

doi:10.5334/jime.1051

Abstract

The rapid integration of artificial intelligence into open education has intensified global demands for equitable access, sustainable infrastructure, and technological inclusion. This study evaluated the feasibility and educational impact of optimizing Open Large Language Models (OLLMs) for deployment in low-resource learning environments. Five open-source models (Falcon, Bloom, GPT-NeoX, T5, and Flan-T5) were optimized using an exploratory-experimental approach with unstructured pruning and Retrieval-Augmented Generation (RAG). The intervention was tested in three simulated educational infrastructures (public university, community digital center, and rural classroom) and analyzed using quantitative metrics on system efficiency and educational output. The findings revealed: (a) a reduction of up to 11% in response time across all models; (b) a decrease of approximately 20% in RAM and VRAM usage; (c) a 1.4% improvement in the educational relevance of responses; and (d) a 33% increase in query throughput, indicating greater scalability in open education contexts. These results offer practical and ethical guidance for educators, policymakers, and technology developers by showcasing how optimized OLLMs can become key enablers of worldwide open, inclusive, and sustainable learning ecosystems.

References

Adiguzel, T, Kaya, MH and Cansu, FK. 2023. ‘Revolutionizing education with AI: Exploring the transformative potential of ChatGPT’. Contemporary Educational Technology, 15(3): ep429. DOI: 10.30935/cedtech/13152
Open DOI Search in Google Scholar Back to article
Aggarwal, D. 2023. ‘Adoption of Artificial Intelligence (AI) for development of smart education as the future of a sustainable education system’. Journal of Artificial Intelligence, Machine Learning, and Neural Networks. DOI: 10.55529/jaimlnn.36.23.28
Open DOI Search in Google Scholar Back to article
Bandi, A, Adapa, PVSR and Kuchi, YEVPK. 2023. ‘The power of generative AI: A review of requirements, models, input–output formats, evaluation metrics, and challenges’. Future Internet, 15(8): 260. DOI: 10.3390/fi15080260
Open DOI Search in Google Scholar Back to article
Bazanova, EM, Gorizontova, AV, Gribova, NN, Chikake, TM and Samosyuk, AV. 2023. ‘Development and prospects of national intelligent system for testing general language competencies deployed through neural network solutions’. Vysshee Obrazovanie v Rossii, 32(8–9): Article 8–9. DOI: 10.31992/0869-3617-2023-32-8-9-147-166
Open DOI Search in Google Scholar Back to article
Beauchemin, P-H and Staley, KW. 2024. ‘The epistemological significance of exploratory experimentation: A pragmatist model of how practices matter philosophically’. European Journal for Philosophy of Science, 14(4): 59. DOI: 10.1007/s13194-024-00620-6
Open DOI Search in Google Scholar Back to article
Benítez, I, Van De Vijver, F and Padilla, JL. 2019. ‘A mixed methods approach to the analysis of bias in cross-cultural studies’. Sociological Methods & Research, 51(1): 237–270. DOI: 10.1177/0049124119852390
Open DOI Search in Google Scholar Back to article
Bevara, R, Lund, BD, Mannuru, NR, Karedla, SP, Mohammed, Y, Kolapudi, ST and Mannuru, A. 2025. ‘Prospects of Retrieval Augmented Generation (RAG) for academic library search and retrieval’. Information Technology and Libraries, in press. DOI: 10.13140/RG.2.2.33848.25607
Open DOI Search in Google Scholar Back to article
Black, S, Biderman, S, Hallahan, E, Anthony, Q, Gao, L, Golding, L, He, H, Leahy, C, McDonell, K, Phang, J, Pieler, M, Prashanth, US, Purohit, S, Reynolds, L, Tow, J, Wang, B and Weinbach, S. 2022. ‘GPT-NeoX-20B: An open-source autoregressive language model’. Proceedings of BigScience Episode #5 — Workshop on Challenges & Perspectives in Creating Large Language Models, 95–136. DOI: 10.18653/v1/2022.bigscience-1.9
Open DOI Search in Google Scholar Back to article
Bura, C and Myakala, PK. 2024. Advancing transformative education: Generative AI as a catalyst for equity and innovation (No. arXiv:2411.15971). arXiv. DOI: 10.48550/arXiv.2411.15971
Open DOI Search in Google Scholar Back to article
Camacho-Zuñiga, C, Rodea-Sánchez, MA, López, OO and Zavala, G. 2024. ‘Generative AI guidelines by/for engineering undergraduates’. 2024 IEEE Global Engineering Education Conference (EDUCON). DOI: 10.1109/EDUCON60312.2024.10578870
Open DOI Search in Google Scholar Back to article
Chan, CKY and Lee, KKW. 2023. ‘The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers?’ Smart Learning Environments, 10(60). DOI: 10.1186/s40561-023-00269-3
Open DOI Search in Google Scholar Back to article
Chatterjee, S and Bhattacharjee, KK. 2020. ‘Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling’. Education and Information Technologies, 25(5): 3443–3463. DOI: 10.1007/s10639-020-10159-7
Open DOI Search in Google Scholar Back to article
Chen, G, Yao, Y, Gao, C-J, Chao, LS, Wan, F and Wong, DF. 2025. ‘Not all LoRA parameters are essential: Insights on inference necessity (versión 1)’. arXiv. DOI: 10.48550/ARXIV.2503.23360
Open DOI Search in Google Scholar Back to article
Chen, RJ, Chen, TY, Lipkova, J, Wang, JJ, Williamson, DFK, Lu, MY, Sahai, S and Mahmood, F. 2022. Algorithm fairness in AI for medicine and healthcare (No. arXiv:2110.00603). arXiv. http://arxiv.org/abs/2110.00603.
Search in Google Scholar Back to article
Cowling, M, Crawford, J, Allen, K-A and Wehmeyer, M. 2023. ‘Using leadership to leverage ChatGPT and artificial intelligence for undergraduate and postgraduate research supervision’. Australasian Journal of Educational Technology, 39(4): 89–103. DOI: 10.14742/ajet.8598
Open DOI Search in Google Scholar Back to article
Dakakni, D and Safa, N. 2023. ‘Artificial intelligence in the L2 classroom: Implications and challenges on ethics and equity in higher education: A 21st century Pandora’s box’. Computers and Education: Artificial Intelligence, 5. DOI: 10.1016/j.caeai.2023.100179
Open DOI Search in Google Scholar Back to article
Das, RJ, Ma, L and Shen, Z. 2024. ‘Beyond size: How gradients shape pruning decisions in large language models’. The Twelfth International Conference on Learning Representations (ICLR 2024), May 7, 2024, Vienna, Austria. https://openreview.net/forum?id=5BoXZXTJvL.
Search in Google Scholar Back to article
de Fine Licht, K. 2023. ‘Integrating large language models into higher education: Guidelines for effective implementation’. IS4SI Summit 2023. DOI: 10.3390/cmsf2023008065
Open DOI Search in Google Scholar Back to article
Demeke, W. 2023. ‘Adoption and use of smart devices as clickers in classrooms in higher education’. Computer Applications in Engineering Education, 31(4): 963–982. DOI: 10.1002/cae.22617
Open DOI Search in Google Scholar Back to article
Emma, L. (2025). Building and training neural networks: Architectures, optimization, and challenges. https://www.researchgate.net/publication/390331788_Building_and_Training_Neural_Networks_Architectures_Optimization_and_Challenges.
Search in Google Scholar Back to article
Ferrara, E. 2023. ‘Should ChatGPT be biased? Challenges and risks of bias in large language models’. First Monday, 28(11). DOI: 10.5210/fm.v28i11.13346
Open DOI Search in Google Scholar Back to article
Ferrer, X, Nuenen, TV, Such, JM, Cote, M and Criado, N. 2021. ‘Bias and discrimination in AI: A cross-disciplinary perspective’. IEEE Technology and Society Magazine, 40(2): 72–80. DOI: 10.1109/MTS.2021.3056293
Open DOI Search in Google Scholar Back to article
Guo, K, Zhong, Y, Li, D and Chu, SKW. 2023. ‘Effects of chatbot-assisted in-class debates on students’ argumentation skills and task motivation’. Computers & Education, 203: 104862. DOI: 10.1016/j.compedu.2023.104862
Open DOI Search in Google Scholar Back to article
Hossain, S, Seyam, TA, Chowdhury, A, Xamidov, M, Ghose, R and Pathak, A. 2025. Fine-tuning LLaMA 2 interference: A comparative study of language implementations for optimal efficiency (Versión 1). arXiv. DOI: 10.48550/ARXIV.2502.01651
Open DOI Search in Google Scholar Back to article
Khatibi, E, Wang, Z and Rahmani, AM. 2025. CDF-RAG: Causal dynamic feedback for adaptive retrieval-augmented generation (Versión 1). arXiv. DOI: 10.48550/ARXIV.2504.12560
Open DOI Search in Google Scholar Back to article
Landers, RN and Behrend, TS. 2023. ‘Auditing the AI auditors: A framework for evaluating fairness and bias in high-stakes AI predictive models’. American Psychologist, 78(1): 36–49. DOI: 10.1037/amp0000972
Open DOI Search in Google Scholar Back to article
Lewis, P, Perez, E, Piktus, A, Petroni, F, Karpukhin, V, Goyal, N, Küttler, H, Lewis, M, Yih, W, Rocktäschel, T, Riedel, S and Kiela, D. 2021. Retrieval-augmented generation for knowledge-intensive NLP tasks (No. arXiv:2005.11401). arXiv. DOI: 10.48550/arXiv.2005.11401
Open DOI Search in Google Scholar Back to article
Luckin, R, Holmes, W, Griffiths, M and Forcier, LB. 2022. ‘Artificial intelligence and the future of learning’. Education and Information Technologies, 27(3): 1805–1823. DOI: 10.1007/s10639-022-10965-y
Open DOI Search in Google Scholar Back to article
Moallemi, EA, Elsawah, S and Ryan, MJ. 2018. ‘Towards an analytical framework for experimental design in exploratory modeling’. 2018 Winter Simulation Conference (WSC), 455–466. DOI: 10.1109/WSC.2018.8632229
Open DOI Search in Google Scholar Back to article
Morris, AMA. 2021. ‘English engineer John Smeaton’s experimental method(s): Optimisation, hypothesis testing, and exploratory experimentation’. Studies in History and Philosophy of Science Part A, 89: 283–294. DOI: 10.1016/j.shpsa.2021.07.004
Open DOI Search in Google Scholar Back to article
Navarrete, R and Luján-Mora, S. 2018. ‘Bridging the accessibility gap in Open educational resources’. Universal Access in the Information Society, 17(4): 755–774. DOI: 10.1007/s10209-017-0529-9
Open DOI Search in Google Scholar Back to article
Parthasarathy, VB, Zafar, A, Khan, A and Shahid, A. 2024. The ultimate guide to fine-tuning LLMs from basics to breakthroughs: An exhaustive review of technologies, research, best practices, applied research challenges and opportunities (versión 3). arXiv. DOI: 10.48550/ARXIV.2408.13296
Open DOI Search in Google Scholar Back to article
Schneegans, S, Lewis, J and Straza, T. 2021. UNESCO science report: The race against time for smarter development. Paris: UNESCO.
Search in Google Scholar Back to article
Singh, A. 2021. An introduction to experimental and exploratory research. Available at: SSRN. DOI: 10.2139/ssrn.3789360
Open DOI Search in Google Scholar Back to article
Spada, I, Giordano, V, Chiarello, F, Abate, M, Dovetto, FM and Fantoni, G. 2023. ‘Tracing topic evolution in higher education: A text mining study on Italian universities’. Studies in Higher Education. DOI: 10.1080/03075079.2023.2284813
Open DOI Search in Google Scholar Back to article
Tijanić Štrok, K. 2025. ‘Artificial neural networks application for the Croatian school maintenance cost estimation’. Tehnički Glasnik, 19(2): 192–202. DOI: 10.31803/tg-20230912130335
Open DOI Search in Google Scholar Back to article
UNESCO. 2021. AI and education: Guidance for policy makers. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000379376.
Search in Google Scholar Back to article
UNESCO. 2023. Global education monitoring report 2023, Southeast Asia: Technology in education: A tool on whose terms? Paris: UNESCO. DOI: 10.54676/RJNF1598
Open DOI Search in Google Scholar Back to article
Youvan, DC. 2025. Retrieval-Augmented Generation (RAG): Advancing AI with dynamic knowledge integration. Preprint. DOI: 10.13140/RG.2.2.30888.89606
Open DOI Search in Google Scholar Back to article

Optimize to Open: An Exploratory-Experimental Approach to the Computational Optimization of Open Large Language Models for Educational Access

Abstract

Paradigm

My account