627 article

 

627

🎤 Texto que debes decir — Slide 1 (Title)

Good morning everyone.

My name is Augusto Paolo Bernal Parraga, and today I will present our research titled:

“Explainable AI-Supported Microlearning Framework for Teacher Digital Competence Development.”

This study explores how artificial intelligence and microlearning strategies can support university teachers in developing digital competence, pedagogical self-efficacy, and responsible instructional integration of generative AI tools such as ChatGPT.

Our work proposes a scalable and technology-mediated professional development framework designed for higher education environments, particularly in contexts with limited institutional AI preparation.

The study was conducted with university teachers from Ecuadorian higher education institutions using a mixed-methods pretest–posttest design.

🎤 Texto que debes decir — Slide 2 (Educational Problem)

The rapid expansion of generative artificial intelligence is transforming higher education worldwide.

However, many university teachers still feel unprepared to integrate AI tools into their teaching practices in a pedagogically effective and ethically responsible way.

In many institutions, AI adoption occurs through isolated experimentation rather than through structured professional development programs.

Teachers frequently report difficulties related to digital competence, instructional integration, academic integrity, and responsible AI use.

At the same time, traditional professional development models are often too long, inflexible, and difficult to scale.

This situation highlights the need for new professional development strategies capable of supporting teachers through flexible, modular, and AI-supported learning environments.

🎤 Texto que debes decir — Slide 3 (Objective)

The main objective of this study was to develop an explainable AI-supported microlearning framework designed to strengthen teachers’ digital competence and the pedagogical integration of generative AI tools in higher education.

The framework combines three main components.

First, modular microlearning design based on short, flexible, and accessible instructional units.

Second, AI-supported learning activities using generative AI tools such as ChatGPT to support teaching and instructional design.

Third, reflective pedagogical development focused on ethical AI use, self-efficacy, and responsible instructional integration.

Our intention was not only to improve technological familiarity, but also to support meaningful, pedagogically grounded, and ethically responsible AI adoption in university teaching practices.

Ultimately, the framework aims to improve instructional quality, teacher confidence, and student-centered learning outcomes in AI-enhanced educational environments.

🎤 Texto que debes decir — Slide 4 (AI-Supported Microlearning Framework)

This slide presents the general architecture of the proposed AI-Supported Microlearning Framework, or MLF.

The framework was designed as an integrated and pedagogically grounded model for teacher digital competence development.

It is organized into four interconnected stages.

First, the input stage considers teachers’ needs, institutional digital environments, and generative AI tools such as ChatGPT.

Second, the design stage incorporates modular microlearning units, AI-supported activities, pedagogical strategies, and assessment design.

Third, the implementation stage delivers the learning experience through digital learning environments using guided AI interaction, reflective activities, and learning analytics.

Finally, the framework generates several educational outputs, including improved digital teaching competence, stronger pedagogical self-efficacy, and more ethical and effective integration of generative AI tools.

An important aspect of the framework is the integration of explainable AI support, which promotes transparency, interpretability, and responsible AI use throughout the learning process.

Overall, the framework aims to empower teachers for more ethical, scalable, and student-centered AI-enhanced higher education environments.

🎤 Texto que debes decir — Slide 5 (Methodology)

This study followed a mixed-methods pretest–posttest research design.

A total of 212 university teachers from a public higher education institution in Ecuador participated in the study.

The intervention consisted of an AI-supported microlearning program delivered through modular and flexible digital learning units.

The framework included guided activities, reflective tasks, AI-supported instructional exercises, and pedagogical integration of generative AI tools such as ChatGPT.

To evaluate the impact of the framework, we collected both quantitative and qualitative data.

Quantitative data included pretest and posttest surveys measuring digital teaching competence, pedagogical self-efficacy, and educational use of generative AI.

Qualitative data included reflective responses and participants’ perceptions regarding AI-supported teaching practices.

Finally, the data were analyzed using statistical tests, effect size analysis, and thematic qualitative analysis to identify changes after the intervention.

 

🎤 Texto que debes decir — Slide 6 (Instructional Modules)

This slide summarizes the six instructional modules included in the AI-supported microlearning program.

Each module was designed as a short, focused, and flexible learning unit lasting approximately 15 to 20 minutes.

The first module introduced the foundations of artificial intelligence in education and its implications for higher education.

The second module explored generative AI tools such as ChatGPT for content generation, feedback, and instructional support.

The third module focused on AI-supported instructional design, including objectives, activities, and assessment strategies.

The fourth module addressed ethical and responsible AI use, including academic integrity, bias, privacy, and ethical decision-making.

The fifth module emphasized pedagogical integration and active learning activities supported by AI tools.

Finally, the sixth module promoted reflection, evaluation, and continuous professional improvement.

Overall, the program was designed to provide practical, accessible, and scalable professional development experiences for university teachers in AI-enhanced educational environments.

🎤 Texto que debes decir — Slide 7 (Results Overview)

This slide presents the main results obtained after the implementation of the AI-supported microlearning framework.

Overall, we observed statistically significant improvements across all evaluated dimensions.

Digital teaching competence increased by approximately 32.7 percent after the intervention.

Pedagogical self-efficacy also improved considerably, with an average increase of 28.9 percent.

The strongest improvement was observed in the educational use of generative AI tools, which increased by more than 41 percent between the pretest and posttest stages.

In addition, the statistical analysis showed highly significant differences, with p-values below 0.001 and large effect sizes across the evaluated dimensions.

These findings suggest that the proposed framework effectively strengthened teachers’ confidence, instructional capabilities, and responsible integration of AI tools into educational practice.

🎤 Texto que debes decir — Slide 8 (Key Findings)

This slide summarizes the most important findings of our study.

First, the AI-supported microlearning framework produced significant improvements across all evaluated dimensions.

Teachers demonstrated stronger digital teaching competence, greater pedagogical self-efficacy, and more confidence in using generative AI tools in educational contexts.

Second, the framework supported more effective integration of AI into teaching practices and instructional design activities.

Participants reported that the modular microlearning format made the learning experience more flexible, accessible, and directly applicable to real classroom needs.

Another important finding was the promotion of ethical and responsible AI use throughout the training process.

Teachers showed greater awareness regarding academic integrity, bias, transparency, and responsible decision-making when using AI technologies.

Overall, the results suggest that explainable AI-supported microlearning can become an effective strategy for preparing educators for the emerging AI-augmented educational ecosystem.

🎤 Texto que debes decir — Slide 9 (Discussion)

The findings of this study are consistent with recent research highlighting the growing importance of AI-supported professional development in higher education.

Previous studies have shown that AI training can improve teachers’ digital competence and instructional confidence.

However, our study extends the literature by combining microlearning strategies, explainable AI, and pedagogically grounded instructional design into a unified professional development framework.

One important contribution of our work is the emphasis on ethical and responsible AI integration rather than purely technological adoption.

The results also demonstrate that modular and flexible microlearning approaches can support scalable professional development models suitable for higher education institutions.

From a broader perspective, the study contributes empirical evidence supporting the use of AI-enhanced professional learning ecosystems for teacher capacity building in the emerging AI era.

Ultimately, the findings suggest that AI-supported microlearning can become an effective and sustainable strategy for advancing instructional quality and educational innovation in higher education.

🎤 Texto que debes decir — Slide 10 (Conclusions)

In conclusion, this study demonstrates that AI-supported microlearning can effectively strengthen teacher digital competence and pedagogical self-efficacy in higher education.

The results showed significant improvements in teachers’ confidence, instructional practices, and educational use of generative AI tools after the intervention.

The framework also promoted more ethical and responsible integration of AI technologies into teaching and instructional design processes.

Another important contribution of the study is the scalability and flexibility of the proposed professional development model, which can be adapted to different higher education contexts.

Overall, the findings suggest that explainable AI-supported microlearning represents a promising strategy for empowering educators and supporting the transition toward AI-augmented educational ecosystems.

Thank you very much for your attention.