Tools for AI-driven Development of Research Competencies
DOI:
https://doi.org/10.62486/latia202316Keywords:
Artificial Intelligence, Research, Data Analysis, Biomedicine, Data PrivacyAbstract
Artificial intelligence (AI) tools are transforming scientific research by enabling the analysis of large volumes of data and the generation of new hypotheses and theoretical models. In 2024, there is an expected proliferation of smaller and more efficient AI models that can run on accessible hardware, facilitating the democratization of access to this technology. This will allow academic institutions and small businesses to implement and optimize AI models without the need for expensive infrastructures. The ability of AI to handle and analyze large datasets has been particularly useful in fields such as biomedicine, where it has accelerated the discovery of new treatments and therapies. Furthermore, the integration of AI models into local devices addresses critical concerns regarding data privacy and security, enabling the secure processing of sensitive information. These tools not only enhance the efficiency and accuracy of research but also foster innovation by expanding the frontiers of knowledge in diverse disciplines.
References
Cardeño-Portela N, Cardeño-Portela EJ, Bonilla-Blanchar E. Las TIC y la transformación académica en las universidades. Región Científica. 2023;2(2):202370. https://doi.org/10.58763/rc202370 DOI: https://doi.org/10.58763/rc202370
Zawacki-Richter, O., Marín, V., Bond, M., & Gouverneur, F. Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education. 2019;16. https://doi.org/10.1186/s41239-019-0171-0 DOI: https://doi.org/10.1186/s41239-019-0171-0
Dogan, M., Dogan, T., & Bozkurt, A. The Use of Artificial Intelligence (AI) in Online Learning and Distance Education Processes: A Systematic Review of Empirical Studies. Applied Sciences. 2023;13(5):3056. https://doi.org/10.3390/app13053056 DOI: https://doi.org/10.3390/app13053056
Velásquez Castro LA, Paredes-Águila JA. Revisión sistemática sobre los desafíos que enfrenta el desarrollo e integración de las tecnologías digitales en el contexto escolar chileno, desde la docencia. Región Científica. 2024;3(1):2024226. https://doi.org/10.58763/rc2024226 DOI: https://doi.org/10.58763/rc2024226
Anantrasirichai, N., & Bull, D. Artificial intelligence in the creative industries: a review. Artificial Intelligence Review. 2022;55:589-656. https://doi.org/10.1007/s10462-021-10039-7 DOI: https://doi.org/10.1007/s10462-021-10039-7
Wang, D., Weisz, J., Muller, M., Ram, P., Geyer, W., Dugan, C., Tausczik, Y., Samulowitz, H., & Gray, A. (2019). Human-AI Collaboration in Data Science. Proceedings of the ACM on Human-Computer Interaction, 3, 1 - 24. https://doi.org/10.1145/3359313 DOI: https://doi.org/10.1145/3359313
Vázquez-Vidal V, Martínez-Prats G. El desarrollo regional y su impacto en la sociedad mexicana. Región Científica. 2023;2(1):202336. https://doi.org/10.58763/rc202336 DOI: https://doi.org/10.58763/rc202336
Park G, Oh H, Lim BC, Khoo BL. Can smart technology make group members more creative? The effect of interactive feedback using sociometric badges on members’ creativity. Behaviour & Information Technology. 2023;42(14):2452-66. https://doi.org/10.1080/0144929X.2022.2126949 DOI: https://doi.org/10.1080/0144929X.2022.2126949
Lau, V., Binnie, A., Basmaji, J., Baig, N., Opgenorth, D., Cameron, S., O’Hearn, K., McDonald, E., Senaratne, J., Sligl, W., Zuege, D., Rewa, O., Bagshaw, S., & Tsang, J. Needs Assessment Survey Identifying Research Processes Which may be Improved by Automation or Artificial Intelligence: ICU Community Modeling and Artificial Intelligence to Improve Efficiency (ICU-Comma). Journal of Intensive Care Medicine. 2022;37:1296-1304. https://doi.org/10.1177/08850666211064844 DOI: https://doi.org/10.1177/08850666211064844
Walters, W., & Murcko, M. Assessing the impact of generative AI on medicinal chemistry. Nature Biotechnology. 2020;38:143-145. https://doi.org/10.1038/s41587-020-0418-2 DOI: https://doi.org/10.1038/s41587-020-0418-2
Noroña González Y, Colala Troya AL, Peñate Hernández JI. La orientación para la proyección individual y social en la educación de jóvenes y adultos: un estudio mixto sobre los proyectos de vida. Región Científica. 2023;2(2):202389. https://doi.org/10.58763/rc202389 DOI: https://doi.org/10.58763/rc202389
Yang, X., Wang, Y., Byrne, R., Schneider, G., & Yang, S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chemical Reviews. 2019;119(18):10520-10594. https://doi.org/10.1021/acs.chemrev.8b00728 DOI: https://doi.org/10.1021/acs.chemrev.8b00728
Collins, G., Dhiman, P., Navarro, C., Ma, J., Hooft, L., Reitsma, J., Logullo, P., Beam, A., Peng, L., calster, B., Smeden, M., Riley, R., & Moons, K. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021;11. https://doi.org/10.1136/bmjopen-2020-048008 DOI: https://doi.org/10.1136/bmjopen-2020-048008
Horváth, I. (2022). AI in interpreting: Ethical considerations. Across Languages and Cultures, 23(1). https://doi.org/10.1556/084.2022.00108 DOI: https://doi.org/10.1556/084.2022.00108
López-Gónzalez YY. Competencia digital del profesorado para las habilidades TIC en el siglo XXI: una evaluación de su desarrollo. Región Científica. 2023;2(2):2023119. https://doi.org/10.58763/rc2023119 DOI: https://doi.org/10.58763/rc2023119
Vittorini, P., Menini, S., & Tonelli, S. (2020). An AI-Based System for Formative and Summative Assessment in Data Science Courses. International Journal of Artificial Intelligence in Education, 31, 159 - 185. https://doi.org/10.1007/s40593-020-00230-2 DOI: https://doi.org/10.1007/s40593-020-00230-2
Creswell JW. Research Design. Qualitative, Quantitative and Mixed Method Approaches. 4 ed2019.
Fisher C. Decoding the Ethics Code: A Practical Guide for Psychologists. 5 ed2023.
Newman M, Gough D. Systematic Reviews in Educational Research: Methodology, Perspectives and Application. Systematic Reviews in Educational Research: Springer; 2019. https://doi.org/10.1007/978-3-658-27602-7_1 DOI: https://doi.org/10.1007/978-3-658-27602-7_1
Casasempere-Satorres A, Vercher-Ferrándiz ML. Bibliographic documentary analysis. Getting the most out of the literature review in qualitative research. New Trends in Qualitative Research. 2020;4:247-57. https://doi.org/10.36367/ntqr.4.2020.247-257 DOI: https://doi.org/10.36367/ntqr.4.2020.247-257
Afanador Cubillos N. Historia de la producción y sus retos en la era actual. Región Científica. 2023;2(1):202315. https://doi.org/10.58763/rc202315 DOI: https://doi.org/10.58763/rc202315
Li, L., Gou, F., Long, H., He, K., & Wu, J. (2022). Effective Data Optimization and Evaluation Based on Social Communication with AI-Assisted in Opportunistic Social Networks. Wireless Communications and Mobile Computing, 2022(1), 4879557. https://doi.org/10.1155/2022/4879557 DOI: https://doi.org/10.1155/2022/4879557
Long, X., Ben, Z., Zeng, X., Liu, Y., Zhang, M., & Zhou, D. Learning Sparse Convolutional Neural Network via Quantization With Low Rank Regularization. IEEE Access. 2019;7:51866-51876. https://doi.org/10.1109/ACCESS.2019.2911536 DOI: https://doi.org/10.1109/ACCESS.2019.2911536
Zapata Muriel FA, Montoya Zapata S, Montoya-Zapata D. Dilemas éticos planteados por el auge de la inteligencia artificial: una mirada desde el transhumanismo. Región Científica. 2024;3(1):2024225. https://doi.org/10.58763/rc2024225 DOI: https://doi.org/10.58763/rc2024225
Buhmann A, Fieseler C. Deep Learning Meets Deep Democracy: Deliberative Governance and Responsible Innovation in Artificial Intelligence. Business Ethics Quarterly. 2022;33:146-179. https://doi.org/10.1017/beq.2021.42 DOI: https://doi.org/10.1017/beq.2021.42
Hauck M, Machhamer R, Czenkusch L, Gollmer K, Dartmann G. Node and Block-Based Development Tools for Distributed Systems With AI Applications. IEEE Access. 2019;7:143109-143119. https://doi.org/10.1109/ACCESS.2019.2940113 DOI: https://doi.org/10.1109/ACCESS.2019.2940113
Nguyen G, Dlugolinsky S, Bobák M, Tran V, García Á, Heredia I, Malík P, Hluchý L. Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey. Artificial Intelligence Review. 2019;52:77-124. https://doi.org/10.1007/s10462-018-09679-z DOI: https://doi.org/10.1007/s10462-018-09679-z
Rahmani A, Azhir E, Ali S, Mohammadi M, Ahmed O, Ghafour M, Ahmed S, Hosseinzadeh M. Artificial intelligence approaches and mechanisms for big data analytics: a systematic study. PeerJ Computer Science. 2021;7. https://doi.org/10.7717/peerj-cs.488 DOI: https://doi.org/10.7717/peerj-cs.488
Muñoz Bonilla HA, Menassa Garrido IS, Rojas Coronado L, Espinosa Rodríguez MA. La innovación en el sector servicios y su relación compleja con la supervivencia empresarial. Región Científica. 2024;3(1):2024214. https://doi.org/10.58763/rc2024214 DOI: https://doi.org/10.58763/rc2024214
Fischer C, Pallavajjala A, Jiang L, Anagnostou V, Tao J, Adams E, Eshleman J, Gocke C, Lin M, Platz E, Xian R. Artificial intelligence-assisted serial analysis of clinical cancer genomics data identifies changing treatment recommendations and therapeutic targets. Clinical cancer research: an official journal of the American Association for Cancer Research. 2022;28(11):2361-2372. https://doi.org/10.1158/1078-0432.CCR-21-4061 DOI: https://doi.org/10.1158/1078-0432.CCR-21-4061
Ginghină O, Hudiță A, Zamfir M, Spânu A, Mardare M, Bondoc I, Buburuzan L, Georgescu S, Costache M, Negrei C, Nitipir C, Gălățeanu B. Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient’s Stratification. Frontiers in Oncology. 2022;12. https://doi.org/10.3389/fonc.2022.856575 DOI: https://doi.org/10.3389/fonc.2022.856575
Arthur F, Hossein K. Deep learning in medical image analysis: a third eye for doctors. Journal of stomatology, oral and maxillofacial surgery. 2019;120(4):279-288. https://doi.org/10.1016/j.jormas.2019.06.002 DOI: https://doi.org/10.1016/j.jormas.2019.06.002
Bhatt C, Kumar I, Vijayakumar V, Singh K, Kumar A. The state of the art of deep learning models in medical science and their challenges. Multimedia Systems. 2020;27:599-613. https://doi.org/10.1007/s00530-020-00694-1 DOI: https://doi.org/10.1007/s00530-020-00694-1
Antunes M, Maximiano M, Gomes R, Pinto D. Information Security and Cybersecurity Management: A Case Study with SMEs in Portugal. Journal of Cybersecurity and Privacy. 2021;1(2):219-38. https://doi.org/10.3390/jcp1020012 DOI: https://doi.org/10.3390/jcp1020012
Palma I, Mendoza M, Milios E. Neural Abstractive Unsupervised Summarization of Online News Discussions. Intelligent Systems and Applications. 2021:822-841. https://doi.org/10.1007/978-3-030-82196-8_60 DOI: https://doi.org/10.1007/978-3-030-82196-8_60
Bhattamisra S, Banerjee P, Gupta P, Mayuren J, Patra S, Candasamy M. Artificial Intelligence in Pharmaceutical and Healthcare Research. Big Data and Cognitive Computing. 2023;7(1):10. https://doi.org/10.3390/bdcc7010010 DOI: https://doi.org/10.3390/bdcc7010010
Mogrovejo Andrade JM. Estrategias resilientes y mecanismos de las organizaciones para mitigar los efectos ocasionados por la pandemia a nivel internacional. Región Científica. 2022;1(1):202211. https://doi.org/10.58763/rc202211 DOI: https://doi.org/10.58763/rc202211
Wamba, S., Bawack, R., Guthrie, C., Queiroz, M., & Carillo, K. (2020). Are we preparing for a good AI society? A bibliometric review and research agenda. Technological Forecasting and Social Change, 164, 120428. https://doi.org/10.1016/j.techfore.2020.120482 DOI: https://doi.org/10.1016/j.techfore.2020.120482
Kitsios F, Kamariotou M. Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda. Sustainability. 2021;13(4):2025. https://doi.org/10.3390/SU13042025 DOI: https://doi.org/10.3390/su13042025
Batra R, Song L, Ramprasad R. Emerging materials intelligence ecosystems propelled by machine learning. Nature Reviews Materials. 2020;6:655-678. https://doi.org/10.1038/s41578-020-00255-y DOI: https://doi.org/10.1038/s41578-020-00255-y
Kammerer-David MI, Murgas-Téllez B. La innovación tecnológica desde un enfoque de dinámica de sistemas. Región Científica. 2024;3(1):2024217. https://doi.org/10.58763/rc2024217 DOI: https://doi.org/10.58763/rc2024217
Wen J, Zhang X, Rush E, Panickan V, Li X, Cai T, Zhou D, Ho Y, Costa L, Begoli E, Hong C, Gaziano J, Cho K, Lu J, Liao K, Zitnik M, Cai T. Multimodal representation learning for predicting molecule–disease relations. Bioinformatics. 2023;39. https://doi.org/10.1093/bioinformatics/btad085 DOI: https://doi.org/10.1093/bioinformatics/btad085
Graffieti G, Borghi G, Maltoni D. Continual Learning in Real-Life Applications. IEEE Robotics and Automation Letters. 2022;7:6195-6202. https://doi.org/10.1109/lra.2022.3167736 DOI: https://doi.org/10.1109/LRA.2022.3167736
Huntingford C, Jeffers E, Bonsall M, Christensen H, Lees T, Yang H. Machine learning and artificial intelligence to aid climate change research and preparedness. Environmental Research Letters. 2019;14. https://doi.org/10.1088/1748-9326/ab4e55 DOI: https://doi.org/10.1088/1748-9326/ab4e55
Coulibaly L, Kamsu-Foguem B, Tangara F. Rule-based machine learning for knowledge discovering in weather data. Future Generation Computer Systems. 2020;108:861-878. https://doi.org/10.1016/j.future.2020.03.012 DOI: https://doi.org/10.1016/j.future.2020.03.012
Ledesma F, Malave-González BE. Patrones de comunicación científica sobre E-commerce: un estudio bibliométrico en la base de datos Scopus. Región Científica. 2022;1(1):202214. https://doi.org/10.58763/rc202214 DOI: https://doi.org/10.58763/rc202214
Feng S, Law N. Mapping Artificial Intelligence in Education Research: a Network‐based Keyword Analysis. International Journal of Artificial Intelligence in Education. 2021;31:277-303. https://doi.org/10.1007/S40593-021-00244-4 DOI: https://doi.org/10.1007/s40593-021-00244-4
Luckin R, Cukurova M. Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology. 2019;50:2824-2838. https://doi.org/10.1111/BJET.12861 DOI: https://doi.org/10.1111/bjet.12861
Ripoll-Rivaldo M. El emprendimiento social universitario como estrategia de desarrollo en personas, comunidades y territorios. Región Científica. 2023;2(2):202379. https://doi.org/10.58763/rc202379 DOI: https://doi.org/10.58763/rc202379
Ju C, Chen A. Identifying Financial Market Trend Reversal Behavior with Structures of Price Activities Based on Deep Learning Methods. IEEE Access. 2022;PP:1-1. https://doi.org/10.1109/ACCESS.2022.3146371 DOI: https://doi.org/10.1109/ACCESS.2022.3146371
Published
Issue
Section
License
Copyright (c) 2023 Chris Nathalie Aristizábal Valbuena (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
