Beyond the Winding Path of Learning: Exploring Affective, Cognitive, and Action-Oriented Prompts for Communication Skills
Project Overview
The document examines the integration of generative AI (GenAI) in education, focusing on its potential to enhance self-directed learning and improve communication skills among learners. It specifically analyzes the effectiveness of three distinct messaging styles—Affective, Cognitive, and Action-Oriented—created using a Large Language Model (LLM). The findings indicate that these styles significantly influence learner perceptions and engagement, which are crucial for improving retention and overall learning outcomes in online educational settings. Evaluator feedback emphasizes a preference for content that is not only evidence-based but also facilitates self-directed engagement, highlighting the importance of personalized learning experiences. Ultimately, the study suggests that leveraging GenAI can lead to more effective educational strategies, fostering an environment where students are empowered to take charge of their learning.
Key Applications
GenAI for instructional messaging
Context: Self-directed learning focusing on communication skills for online learners
Implementation: Utilized ChatGPT to generate 30 instructional items across three messaging styles and evaluated their desirability and appropriateness
Outcomes: Increased engagement, clearer instructional content, improved learner perceptions of skill-building experiences
Challenges: Initial negative reactions from learners due to lack of shared understanding and cognitive frameworks
Implementation Barriers
Cognitive Barrier
Learners may struggle with establishing a shared understanding of the content, leading to resistance and negative perceptions.
Proposed Solutions: Incorporate strategies to facilitate cognitive, affective, and action-oriented learning, and enhance the agent's social features to foster common ground.
Content Design Barrier
Overly affective content may lead to disengagement, and learners may have different expectations from AI versus human interactions.
Proposed Solutions: Balance affective elements in system interactions while focusing on cognitive and action-oriented instructional content.
Project Team
Naoko Hayashida
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Naoko Hayashida
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai