Brayan Díaz
Especialidad:
Uso de IA para abordar problemas críticos en educación
Email:
brayan.diaz@cenia.cl
Brayan es doctorado en aprendizaje y enseñanza en STEM de la Universidad Estatal de Carolina del Norte, Estados Unidos. Actualmente, también se desempeña como investigador asociado en la misma universidad. Su investigación multidisciplinaria se centra en el uso de tecnología para abordar problemas críticos en educación: acceso y equidad en educación, establecer las bases para el impacto y la promesa de la IA para facilitar prácticas centradas en el estudiante, uso de la IA en la educación rural, integración de tecnología de aprendizaje para apoyar y mejorar la transición de la universidad al trabajo, desarrollo de nuevas teorías de aprendizaje para guiar la implementación de la IA en la educación.
Su investigación ha sido publicada en prestigiosas revistas como Computers &Education, Higher Education, Chemical Education Research and Practice, y Journal of Research in Science Teaching.
PUBLICACIONES
In response to the lack of field experience perceived by employers and recent STEM graduates, Work-Integrated Learning (WIL) arrangements have become more common. However, not all WIL experiences are valuable. Indeed, having students in companies without establishing protocols to protect their participation, meaningful interaction, and psychological safety could be counterproductive to non-traditional students. Furthermore, WIL experiences must be designed purposefully to ensure equitable learning experiences for all students. This paper applies an inductive thematic analysis of interviews with 19 current and 4 former students, interviews with current and former workers at host worksites, 76 course surveys, and a total of 15 memos collected from class and workplace observations from graduate engineering classes designed under WIL arrangements between 2017 and 2022. Using Communities of Practice (CoP), we conceptualize WIL experiences as a connection between two communities, generating a theory-based understanding of different possible types of connection, how to establish those connections, and how they contribute to student learning. Building from student experiences, vital elements to integrate workplace learning into the curriculum effectively were identified. Leveraging CoP theory concepts, we describe various modes of integrating workplace experiences into the curriculum, supporting students’ increased participation in both settings and allowing them to become brokers who can transfer ideas and knowledge and acquire new skills. From this, we propose a scaffolding model to implement WIL, which enables teachers to select from among several effective strategies that adapt to students' unique contexts, prior experiences, and learning objectives. For all strategies, guidance is provided about designing WIL experiences that progressively immerse students as engaged, active participants in companies.
This paper describes research into two pedagogical approaches to foster transdisciplinarity in a graduate engineering course that involves education and computer science. Leveraging the Communities of Practice framework, we examine how students majoring in computer science can integrate new knowledge from education and computer science to engage in an educational data mining project. The first course iteration sought to connect students from education and computer science disciplines through a blend of problem-based learning and traditional lectures. The second course iteration involved computer science students only, but included two instructors, one from computer science and the other from education. To evaluate these approaches, we conducted multiple student interviews and classroom observations.
Policy documents call for supporting STEM students in developing collaborative abilities for working in multidisciplinary teams. Courses with intra- and inter-group collaboration are therefore essential to prepare STEM students to participate in modern multidisciplinary professional environments. To analyze those courses, this paper develops a novel theoretical framework of the levels of functioning (individual, within team, across team, and whole group) that may occur. Using the novel framework as well as Communities of Practice theory and Social Interaction Theory, we analyzed a graduate engineering course that communicated through the Slack platform, using a case study design to examine students’ interaction. Social Network Analysis of 5969 Slack messages exchanged through the semester on channels for individual teams, sets of teams, and the entire class was complemented with qualitative analysis of interviews, class materials, observations, and the content of Slack messages. Findings reveal distinct patterns of intra- and inter-group participation. This study highlights how groups interacted through brokers, boundary objects, and tools. Moreover, subsets of teams displayed extensive interaction concerning related tasks, exemplifying “overlap” connections. Diverse patterns of brokerage were characterized. This paper concludes with a general approach for evaluating courses with multi-group collaboration. This approach can be used to diagnose complex multi-team classes, especially in hybrid or online courses where communication occurs through online platforms. This methodology holds promise for promoting effective collaboration and fostering teamwork skills among students in STEM fields.
Publisher: Journal of Research in Science Teaching Link>
ABSTRACT
Artificial intelligence (AI) technologies generate increasingly sophisticated non-human cognition; however, foundational learning theories only contemplate human cognition, and current research conceptualizes AI as a pedagogical tool. We argue that the incipient abilities of AI for mutual engagement with people could allow AI to participate as a legitimate member in social constructivist learning environments and suggest some potential structures and activities to explore AI's capabilities for full participation.
"Participation is an active process, but I will reserve the term for actors who are members of social communities. For instance, I will not say that a computer “participates” in a community of practice…. (Wenger, 1998, p. 56)"
Twenty-five years ago, Etienne Wenger published his influential book Communities of practice: Learning, meaning, and identity (Wenger, 1998), where he specifically discounted computers as potential members of a community of practice (CoP). Recently, however, the abilities of computational systems like generative artificial intelligence (AI) oblige us to reconsider the roles non-human cognition could play in communities of practice centered on learning. Recently, the editorial article “Artificial Intelligence and the Journal of Research in Science Teaching” (Sadler et al., 2024) describes the potential for AI technology to transform science education, but notes that “the science education research community is not as far along as it needs to be in terms of understanding, theorizing, and studying the intersections of AI and science education.” (p. 742). In response, this commentary presents our theorization and conceptualization of AI in science education. We apply the lens of social constructivism (Wenger, 1998) to theorize about this question and we argue that the nature of generative AI allows it to transcend an instrumental role and achieve full participation in a CoP. We are convinced that socio-constructivist theory in general, and CoP specifically, can provide conceptual tools and theoretical underpinnings to guide the use of AI in education. In this commentary, we synthesize ideas from current literature to construct a theoretical framework and offer suggestions for the transformative use of generative AI.
Publisher: Journal of Engineering Education Link>
ABSTRACT
Background
We examine the efficacy of an online collaborative problem-solving (CPS) teaching approach in academic performance and student connections with other peers, among first-year engineering calculus students at a Latin American university. Our research uses communities of practice (CoP) to emphasize the social nature of learning and the importance of participation and interaction within a community.Methods
The work applies a quasi-experimental design and social network analysis (SNA). A total of 202 engineering students were instructed using CPS methodology (experimental group), while 380 students received traditional online teaching methods (control group) during one semester in the first calculus class for engineers.Results
Results show no significant difference in the grades obtained between the experimental and control groups. However, students exposed to CPS reported a statistically significant higher passing rate, as well as larger and more significant academic and social connections. Additionally, SNA results suggest that CPS facilitated stronger peer connections and promoted a more equitable distribution of participation among students, particularly women, compared to students taught under traditional online teaching methods.Conclusions
The study underscores the importance of fostering collaborative learning environments and highlights CPS as a strategy to enhance student performance and network formation. Findings suggest that CPS can improve academic outcomes and promote more equitable learning practices, potentially reducing dropout rates among women engineering students. These findings contribute to the ongoing efforts to address systematic biases and enhance learning experiences in engineering education.
We examine the efficacy of an online collaborative problem‐solving (CPS) teaching approach in academic performance and student connections with other peers, among first‐year engineering calculus students at a Latin American university. Our research uses communities of practice (CoP) to emphasize the social nature of learning and the importance of participation and interaction within a community.
Pre-service teachers can play a crucial role in integrating AI-based tools into the new educational landscape. However, there is a need to validate specialized instruments, apply current conceptualizations such as intelligent-TPACK, and address ethical issues, as pre-service teachers are often overlooked in the development of tools for AI integration. To address these gaps, we adapted a previously existing instrument designed for in-service teachers to measure pre-service teachers’ integration of AI within their training context. We conducted a quantitative cross-sectional survey with a total of 366 pre-service teachers to evaluate the adapted intelligent-TPACK instrument and examine participants' demographic characteristics related to the framework dimensions. Data analysis included a Confirmatory Factor Analysis to assess the factor model of the adapted instrument, followed by correlations to compare participant variables such as gender, type of university, and stage in the training program with the Intelligent-TPACK model factors. To investigate the differences among groups, the nonparametric ANCOVA test (Quade test) was utilized, enabling the control of covariates like age and academic progress level to ensure comparability across the dimensions of the Intelligent-TPACK model. Findings reveal a high fit of the Intelligent-TPACK model for pre-service teachers (CFI=0.997; TLI=0.997). The data also shows statistically significant effects related to academic progress level and type of institution, while factors -gender, geographic location, and type of major- did not demonstrate noteworthy differences. These results highlight key areas for future curriculum development and support for pre-service teachers in integrating AI education.
This research investigates the challenges and opportunities rural elementary teachers perceive in using AI as a pedagogical tool to support student learning in rural schools. Using a convergent parallel mixed methods approach, we analyzed the responses from 45 rural teachers who participated in professional development on AI integration in rural education. Through both closed-ended and open-ended survey responses, we employed an adaptation of the TPACK framework (I-TPACK) and the AI literacy framework proposed by UNESCO to identify the primary challenges and opportunities in utilizing AI for pedagogical purposes in rural education. The results highlight resource accessibility and teacher professional development as critical challenges and opportunities to reduce the digital divide in rural communities. Teachers see the inclusion of AI as an opportunity to personalize learning, reduce workload, and facilitate teaching in multigrade classrooms without perceiving it as a job threat. At the same time, they emphasize the need for technological and didactic resources aligned with the specific characteristics of their contexts, such as offline resources and adaptable AI curricula to address the prevalent issue of limited or absent internet connectivity in many rural schools.
This research examines generative AI (GenAI) use in a university course that encouraged ChatGPT for specific assignments. Using the Pedagogical Centered AI (PCAI) framework, we explore how students perceive, use, and position ChatGPT, and how usage patterns influenced performance. Students utilized ChatGPT during the latter half of the Spring 2024 semester. Comparisons were made with the first half of the course and prior iterations (2022 and 2023) without GenAI. All students in the 2024 cohort — 40 students — were invited to participate in the study. Data includes 18 student interviews from the 2024 cohort and student work from all iterations. Interviews underwent qualitative deductive thematic analysis using PCAI’s predefined codes PCAI frames AI in education through six learning theories: behaviorism, cognitivism, constructivism, social constructivism, experiential learning, and communities of practice. Class materials and academic records were analyzed to assess performance quantitatively using inferential statistics. Findings reveal students predominantly view AI from a behaviorist perspective: as a tool for completing tasks. Some aligned usage with cognitive learning theory by using AI to reduce cognitive load, while others adopted social constructivist or constructivist perspectives, using AI to build understanding through feedback and exam preparation functions. Overuse of ChatGPT correlated with lower grades, though only one student acknowledged its negative impact on learning. We discuss implications for higher education and highlight how ChatGPT supports diverse teaching and learning approaches tailored to students’ needs. In particular, strategies aligned with constructivism, social constructivism, and communities of practice approaches seem to enhance student learning. However, behaviorist approaches to AI use could hinder learning outcomes. Although most students were aware of the negative impact of AI overuse, they also mentioned that minimal training and explanation were provided in other classes, highlighting the need for a more comprehensive program to support AI literacy in higher education.
This study investigates the impact of using engineering design as a conceptual framework for developing interdisciplinary sustainable project-based learning experiences in K–12 rural settings to support the early stages of the materials science pipeline. Rural students face disparities in STEM access and opportunities compared to their urban counterparts, and teachers receive less training and have fewer practical strategies that adapt to their specific contextual needs. Despite the challenges of rural schools (e.g., limited resources and multigrade classrooms), rural contexts offer opportunities to implement more hands-on, rich experiences built through engineering design to support sustainable practices. We present findings from a professional development initiative involving 25 rural teachers who collaboratively designed and implemented STEM projects focused on energy, sustainability, materials science, and science disciplines. Knowledge tests were administered to 146 rural students from 16 rural schools in southern Chile. The study draws on semistructured interviews, classroom observations, and implementation reflections to evaluate the effectiveness of these experiences. Three key contributions emerge: (1) evidence that the engineering design process can be successfully adapted for multigrade, resource-limited environments; (2) the codevelopment of content-validated instruments that scaffold teachers’ and students’ understanding of science and sustainability concepts; and (3) students and teachers reported feeling motivated and successful and viewed these strategies as beneficial for future STEM practices. This research offers practical insights into how engineering design can enhance STEM education in underserved rural communities.
Edificio de Innovación UC, Piso 2
Vicuña Mackenna 4860
Macul, Chile
Vicuña Mackenna 4860
Macul, Chile