(In)Competences of Education Researchers in the Era of Generative AI
António Pedro Costa, University of Aveiro (Portugal)
Researcher at the Research Centre on Didactics and Technology in the Education of Trainers (CIDTFF), Department of Education and Psychology, University of Aveiro, and collaborator at the Laboratory of Artificial Intelligence and Computer Science (LIACC), Faculty of Engineering, University of Porto.
Isabel Pinho, University of Aveiro (Portugal)
Researcher at the University of Aveiro, her current work is related to Research Assessment, Learning Assessment, Networks, Literature Review.
Yakamury Lira, University of Aveiro (Portugal)
Researcher at the University of Aveiro.
Researchers in the field of social and human sciences are quite enthusiastic about how artificial intelligence (AI) tools can enhance research. It is quite appealing for a researcher to know how to use tools to improve productivity and objectivity by overcoming human limitations, referred to in this text as (in)competencies. There are numerous tools, primarily generative artificial Intelligence (GenAI), that assist the researcher in the different phases of a research project.
In the article “Artificial Intelligence and illusions of understanding in scientific research”, the authors Messeri and Crockett (2024) attempt to explain why AI tools are so attractive and what the risks are of implementing them throughout the research process. The same authors argue that the available AI solutions can also submerge our cognitive limitations, making researchers vulnerable to illusions of understanding. Continuing to explore the aforementioned article, the authors assert that such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which certain types of methods, questions, and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. (Raj, 2024; Roberts, 2024). The proliferation of AI tools in science may have the potential to increase the productivity of scientific research, but it may not enhance the in-depth understanding of the subjects in question (Andrade-Girón et al., 2024; Roberts, 2024). We provide a framework to advance discussions on responsible knowledge production in the AI era by analyzing the use of these tools.
In this context, a working group was established to develop tools that enable higher education professionals, mainly teachers, students, and researchers, to identify the transversal and specific competencies that should be explored in research projects in the field of social and human sciences, with a greater emphasis on education. For example, from a post-humanist perspective, the aim is to examine how the application of research methodologies and AI by the researcher influences the subjects and objects of research, considering that ethical responsibility lies with the human researcher.
The perceived competencies of researchers become essential in the entire development and deepening of possible new competencies. According to Woods and colleagues (2016) in the article “Researcher reflexivity: exploring the impacts of CAQDAS use” in the context of CAQDAS (Computer-Assisted Qualitative Data Analysis), the investigator’s behavior should not be dominated by the “behavior” of language models.
In this sense, the working group advanced with the bibliometric study, presented at a conference titled “Bibliometric and Comparative Analysis of Generative Artificial Intelligence in Education Research.” (2024). This bibliometric analysis provided a global perspective on generative Artificial intelligence (GenAI) in education research, using the Scopus and Web of Science databases. (WoS). Subsequently, in the writing of the book chapter “Competence Frameworks for Exploring Generative AI in Education” to be published by Cabi in 2025, the first explanations were presented, focusing on the design of competence frameworks for equitable and inclusive implementation. In this chapter, a competency model is proposed, emphasizing critical thinking, problem-solving, collaboration, ethics, and continuous learning as essential for the integration of GenAI. It also emphasizes the importance of transparency, ethical decision-making, and collaboration among stakeholders involved in education in/with AI. Ensuring inclusion and equity is crucial, considering cultural influences and incorporating diverse perspectives in curriculum development. The chapter concludes by addressing the challenges and opportunities in integrating GenAI tools in educational environments, emphasizing the need for comprehensive training, motivation, and continuous support for educators and students. Globally, the chapter encourages the responsible use of GenAI to improve learning experiences and promote inclusive education.
Given this scenario, the continuous training of researchers emerges as an essential pillar to ensure the conscious and ethical use of AI tools (Flores-Vivar & García-Peñalvo, 2023). By promoting a culture of reflexivity, critical thinking, and ethics, it is possible to mitigate negative effects and ensure that generative AI is a tool that complements, advises, and does not replace human capabilities, countering the risks pointed out by Monteiro et al. (2024) and Roberts (2024). Thus, the balanced integration between the potential of AI and the competences of researchers will be crucial to ensure that the future of educational research is more inclusive, innovative, and diverse (Guárdia Ortiz et al., 2024; Kadaruddin, 2023). By enabling researchers to interpret and question the numerous possibilities of results generated by GenAI, it is possible to avoid biases and erroneous interpretations that could negatively impact the quality of the research.
In summary, on one hand, we have an optimistic scenario, in which researchers use GenAI collaboratively and critically, enhancing innovation and ensuring the inclusion of different perspectives, and on the other hand, a cautious scenario, where excessive dependence on GenAI compromises methodological diversity and the quality of research projects. The path to be followed will depend on our ability to mediate human competences with the power of technology, always aiming for an equitable, ethical, and responsible landscape of the knowledge we are building.
References
Andrade-Girón, D., Marín-Rodriguez, W., Sandivar-Rosas, J., Carreño-Cisneros, E., Susanibar-Ramirez, E., Zuñiga-Rojas, M., Angeles-Morales, J., & Villarreal-Torres, H. (2024). Generative artificial intelligence in higher education learning: A review based on academic databases. Iberoamerican Journal of Science Measurement and Communication, 4(1), 1–16. https://doi.org/10.47909/ijsmc.101
Baek, E. O., & Wilson, R. V. (2024). An Inquiry Into the Use of Generative AI and Its Implications in Education. International Journal of Adult Education and Technology, 15(1), 1–14. https://doi.org/10.4018/IJAET.349233
Faraasyatul’Alam, G. F., Wiyono, B. B., Burhanuddin, B., Muslihati, M., & Mujaidah, A. (2024). Artificial Intelligence in Education World: Opportunities, Challenges, and Future Research Recommendations. Fahima, 3(2), 223–234. https://doi.org/10.54622/fahima.v3i2.350
Flores-Vivar, J.-M., & García-Peñalvo, F.-J. (2023). Reflections on the ethics, potential, and challenges of artificial intelligence in the framework of quality education (SDG4). Comunicar, 31(74), 37–47. https://doi.org/10.3916/C74-2023-03
Guárdia Ortiz, L., Bekerman, Z., & Zapata Ros, M. (2024). Presentación del número especial “IA generativa, ChatGPT y Educación. Consecuencias para el Aprendizaje Inteligente y la Evaluación Educativa.” Revista de Educación a Distancia (RED), 24(78). https://doi.org/10.6018/red.609801
Kadaruddin, K. (2023). Empowering Education through Generative AI: Innovative Instructional Strategies for Tomorrow’s Learners. International Journal of Business, Law, and Education, 4(2), 618–625. https://doi.org/10.56442/ijble.v4i2.215
Lima, L. A. de O., Gomes, L. P., Silva, P. H. da S. e, Oliveira, E. F. da S., Nascimento, M. do, Tourem, R. V., Gonçalves, J. N. de A., Lima, A. da S., Sobral, R., & Santos, I. da M. P. dos. (2024). Artificial intelligence and its use in the educational process. In Navigating through the knowledge of education. Seven Editora. https://doi.org/10.56238/sevened2024.002-043
Monteiro, E. L., dos Santos, A. A., da Silva, J. A., de Oliveira, A. A., Monteiro, R. R., de Campos, M. C. V., Sousa, T. S. R., Borba, L. M., Machado, M. L., & das Graças Lacerda da Cunha, D. (2024). Inteligência artificial na educação: aplicações e implicações para o ensino e a aprendizagem. Caderno Pedagógico. https://api.semanticscholar.org/CorpusID:269038444
Moresi, E. A. D., Pinho, I., Costa, A. P., Burneo, P. S., Machado, L. B., & Freitas, F. M. (2024). Bibliometric and Comparative Analysis of Generative Artificial Intelligence in Education Research. 19th Iberian Conference on Information Systems and Technologies (CISTI), no prelo.
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