Instructional design 4PADAFE MOOC V3

The dataset compiled for this study consists of qualitative evidence gathered from the active participation of systems engineering students engaged in the instructional design of a Massive Open Online Course (MOOC). The design process was guided by the 4PADAFE methodology and enriched by the use of generative artificial intelligence (AI) tools. This dataset does not merely capture isolated opinions but provides a deep and comprehensive account of students’ perceptions, reflections, and collaborative experiences during the different phases of the project.

Data were primarily collected through semi-structured interviews, focus group discussions, and reflective journals, all of which offered a multiplicity of perspectives on how participants interacted with the instructional design framework. Each of these sources was carefully transcribed and coded to ensure accuracy and preserve the authenticity of the narratives. The interviews and group discussions were intended to reveal participants’ lived experiences and the ways in which they perceived the potential, limitations, and implications of combining pedagogical methodologies with AI-driven tools such as ChatGPT, DALL·E, and Gamma. Meanwhile, the reflective journals provided longitudinal insights into the evolving thought processes, emotional responses, and decision-making strategies employed by students throughout the project.

The dataset is characterized by its richness and contextual depth. Instead of focusing on statistical generalization, the information reflects the complexity of the educational innovation process. It sheds light on how the 4PADAFE methodology, structured around phases such as strategic planning, instructional design, and material production, served as a scaffold for students to experiment with different didactic strategies. Simultaneously, it highlights the transformative role of generative AI in supporting creativity, accelerating content creation, and promoting critical reflection on ethical considerations surrounding the integration of new technologies into education.

Another distinctive feature of the dataset is its ability to capture interactional and collaborative dimensions. Group discussions reveal how students negotiated meaning, divided tasks, and resolved conflicts when working with AI-based tools. These elements allow the dataset to provide a holistic understanding not only of individual experiences but also of the social dynamics inherent in the process of designing a MOOC. By encompassing cognitive, affective, and social dimensions, the data facilitate an in-depth exploration of how technological and pedagogical frameworks intersect in real-world learning design scenarios.