LivePBL Ada-Rob Skill Communication Model

Current Research Development of LivePBL DEEP Method

The LivePBL DEEP Method is currently being piloted and tested to address the diverse and evolving needs of students. A key innovation within this design method is the LivePBL Ada-Rob Skill Communication model, which stands for Adaptive Role-Based Skill Communication. This model aims to enhance the design of a Gen AI application in educational contexts at a layer of role-based communication so as to enhance learning outcomes by leveraging adaptive AI technologies within a project-based learning (PBL) approach.

This article introduces the LivePBL DEEP method, explains the difference between skill communication and communication skills within this context, describes the concept of Ada-Rob, and explores how Gen AI interacts with roles in vocal training to enable adaptive learning.

The LivePBL DEEP method is a design method to enhance PBL by integrating adaptive technologies and a structured approach to learning. DEEP stands for Direction, Education, Event, and Project:

• Direction: This phase focuses on setting personalized learning goals and ensuring that educational projects are aligned with real-world relevance.
• Education: This phase emphasizes the integration of academic content with essential 21st-century skills, ensuring learners critically and contextually engage with the material.
• Event: This phase involves dynamic, real-world events that make learning more engaging and contextually relevant.
• Project: This phase focuses on the design and execution of educational projects, providing clear guidance and tools for effective project management.

The DEEP method aims to create a comprehensive learning environment that bridges the gap between theoretical knowledge and practical applications, fostering deeper engagement and better learning outcomes.

The Difference Between Skill Communication and Communication Skills

Within the DEEP design process, it's important to differentiate between "skill communication" and "communication skills":

• Communication Skills: These are the abilities individuals develop to effectively convey and receive messages, including listening, speaking, reading, and writing. Traditional educational methods often focus on these skills in isolation.
• Skill Communication: This refers to the methodology and strategies used to design and implement effective communication practices within educational programs. It involves creating adaptive and context-sensitive communication processes that enhance the teaching and learning experience. In the context of LivePBL Ada-Rob, skill communication is the foundation for developing adaptive learning environments where communication strategies are tailored to the specific needs of the learners.

Understanding the Ada-Rob Concept

The Ada-Rob (Adaptive Role-Based) concept is central to the LivePBL Ada-Rob Skill Communication model. It is a communication model that dynamically assigns roles to AI agents based on real-time feedback and the specific needs of the educational context. This model operates on multiple layers, each corresponding to different roles that the AI agent can assume, such as vocal coach, technique advisor, practice monitor, and performance evaluator.

Key Elements of Ada-Rob:

• Adaptive Role Assignment: AI agents can switch roles dynamically, depending on the learning context and feedback from students and educators. This flexibility ensures that the educational experience is personalized and responsive to the needs of each learner.
• Layered Communication: The model integrates cognitive, social, and ecological domains to create a holistic learning environment. This layered approach ensures that learning is not only effective but also contextually relevant and engaging.
• Skill Communication: By focusing on the methodology behind communication, Ada-Rob ensures that the design and implementation of educational programs are effective and adaptive.

How Gen AI DEEP Method Design Interacts with Roles to Enable Adaptive Learning

Gen AI, such as ChatGPT, plays a crucial role in the Ada-Rob model by enabling adaptive learning through its dynamic role assignments. Here’s how Gen AI interacts with roles within the Ada-Rob framework, specifically in the context of vocal training.

Dynamic Role Adaptation

Gen AI can adapt its role based on the real-time needs of the students and the educational environment. For example:

• Vocal Coach Role: When a student needs help with vocal techniques, the AI can switch to a coaching role, providing guidance on breath control, pitch accuracy, and vocal exercises tailored to the student’s current level and goals.
• Technique Advisor Role: During practice sessions, the AI can offer advice on specific techniques, such as vibrato, articulation, and phrasing, helping students refine their skills with targeted feedback.
• Practice Monitor Role: The AI can track a student’s practice routines, offering reminders, suggesting adjustments, and ensuring consistency in practice habits. It can also provide motivational feedback to keep students engaged and progressing.
• Performance Evaluator Role: After a performance or practice session, the AI can evaluate the student’s performance, providing detailed feedback on strengths and areas for improvement. This role is crucial for preparing students for real-world performances and auditions.

Real-Time Feedback and Adaptation

Gen AI excels in providing real-time feedback and adapting its interactions based on this feedback. This capability is essential for creating a responsive and engaging learning environment. For instance, if a student is struggling with a particular technique, the AI can adjust its guidance, provide additional examples, or slow down the pace of instruction to ensure comprehension.

Integration with Educational Standards

The adaptive functions of Gen AI are aligned with educational standards and domains (cognitive, social, and ecological). This alignment ensures that the learning experiences are not only personalized but also meet the required educational outcomes. For example, the AI can track a student’s progress against specific learning objectives and adjust its interactions to help the student achieve these goals.

Enhancing Project-Based Learning

In the context of project-based learning in vocal training, Gen AI can support the entire project lifecycle—from planning and execution to evaluation. It can help students set clear objectives, manage practice timelines, and provide continuous feedback to ensure successful project completion. By integrating AI into project-based learning, the LivePBL Ada-Rob model ensures that learning is practical, hands-on, and aligned with real-world applications.

Conclusion

The LivePBL Ada-Rob Skill Communication model represents a significant advancement in educational methodology, leveraging the power of adaptive AI to create a dynamic and responsive learning environment. By differentiating between skill communication and communication skills and by employing the Ada-Rob model, this model addresses the diverse and evolving needs of students. The integration of Gen AI DEEP Method within this model enhances the ability to provide personalised, real-time, and contextually relevant learning experiences, ultimately leading to better educational outcomes. As educational practices continue to evolve, the LivePBL Ada-Rob model offers a robust and innovative approach to meeting the challenges of modern education.