An Exploration of Constructionist Learnings and Artificial Intelligence in Keynesian Representation

Potential Abstract:

Abstract: With the rapid advancements in artificial intelligence (AI) and the growing emphasis on constructivist approaches in education, there is an increasing interest in exploring the potential of AI-based tools to enhance learning experiences. This study investigates the integration of constructionist learnings and AI technologies through a Keynesian representation framework, aiming to understand its impact on student engagement and knowledge acquisition.

The research adopts a mixed-methods approach, employing both quantitative and qualitative measures to capture a holistic view of the phenomenon. Participants include middle school students from diverse socioeconomic backgrounds, who engage in a series of learning activities facilitated by an AI-based constructionist learning environment. The environment provides personalized learning experiences by tailoring instructional strategies to individual students’ needs and employing AI algorithms to adapt content delivery.

The study focuses on two key dimensions: student engagement and knowledge acquisition. Student engagement is assessed through self-report measures, classroom observations, and analysis of interaction patterns within the AI-based learning environment. Knowledge acquisition is measured by pre- and post-tests, evaluating students’ understanding of Keynesian economic concepts and their ability to apply them in real-world scenarios.

Preliminary findings suggest that the integration of constructionist learnings and AI technologies can significantly enhance student engagement. Students reported heightened motivation and interest in the learning activities, attributing it to the interactive and personalized nature of the AI-based environment. Classroom observations further indicate increased levels of active participation and collaboration among students.

Regarding knowledge acquisition, initial results indicate positive outcomes, with students demonstrating a deeper understanding of Keynesian economic principles compared to traditional instructional approaches. The personalized nature of the AI-based environment allowed students to explore complex concepts at their own pace, receive immediate feedback, and reflect on their learning process.

This research contributes to the existing literature on constructionist approaches, AI in education, and Keynesian representation. By combining constructivist pedagogies, AI technologies, and a Keynesian framework, this study uncovers new possibilities for instructional design and personalized learning experiences in the field of economics education.

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