Orientating Inclusive Education in Practice: How the LivePBL DEEP Method and Generative AI Could Bridge the Gaps

In the ever-evolving landscape of education, achieving equality is a constant goal, yet addressing the complexities of diversity that arise from inequality presents significant challenges. Despite well-intentioned policies fostering inclusion and diversity, there still needs to be a significant gap between these policies and their practical implementation. This gap mainly affects marginalised groups facing barriers that limit their access to quality education. Addressing this challenge requires more than just policy adjustments; It necessitates innovative approaches to turn these policies into actionable outcomes, balancing both sides: standardised equality with the diversity that emerges from the positive aspects of inequalities.

An innovative approach emerging from this research and development, driven by several voluntary academic and social communities led by China, is the LivePBL DEEP Method. This method is currently reviewed in several academic book chapters. It has evolved in response to COVID-19 and the urgent need for more inclusive education systems that effectively address educational relationships between inequalities and diversity. LivePBL stands for "Live Project-Based Learning," emphasising experiential and community actual learning projects directly applicable to real-world scenarios. DEEP is an acronym representing the four phases of this method of design orientation: Direction, Education, Event, and Project. Each phase is designed to systematically address different aspects of inequality in higher education, making the process more structured and actionable.

1. Direction: This phase focuses on setting clear goals and defining desired outcomes to tackle inequalities in education. It involves creating a design orientation that aligns with the needs of all stakeholders, particularly those from disadvantaged backgrounds.
2. Education: In this phase, the method identifies educational contexts of inequality and designs curricula and resources that leverage digital technologies and Gen AI for co-learning opportunities. The goal is to create learning environments that are inclusive and responsive to students' diverse needs.
3. Event: This phase involves engaging students in learning through meaningful activities and experiences. Utilising Gen AI personalises learning pathways and fosters a more engaging and supportive educational environment.
4. Project: The final phase emphasises applying what has been learned to real-world issues. This phase focuses on implementing practical projects that help bridge the gap between inequality and equality in higher education.

An essential critical component of the LivePBL DEEP Method is its integration of Gen AI. By leveraging tools like ChatGPT, the method introduces personalised and adaptive learning experiences tailored to each student's unique needs. This approach aims to make education more inclusive, particularly for those traditionally underserved. Through Gen AI, students receive customised support that are based through their learning preferences, thereby enhancing their educational journey and providing additional assistance to those who need it the most.

The method also advocates for hybrid learning environments that blend formal and non-formal education. In doing so, it coordinates the power of digital technologies to expand access to educational resources, making learning opportunities not just more accessible but more facilitated to a broader audience. This hybrid approach is especially relevant in the post-COVID-19 era, where traditional educational structures have been disrupted, and there is a growing need for hybrid, flexible and inclusive learning models.

What sets the LivePBL DEEP Method apart is its focus on designed actual-world applications dedicated to the real world. The method isn't just staying in theory; it has been under testing and applied in various hybrid settings, demonstrating its effectiveness in improving inclusion and educational outcomes for disadvantaged groups. There have been designed practical use cases that serve as powerful examples of how the DEEP Method can be implemented to create more equitable educational environments.

However, as promising as the LivePBL DEEP Method is, it has challenges. The current research addresses the critical issues of scalability and ethical considerations. For instance, while Gen AI can be a game-changer in education, the looming challenge of the digital divide is still present. Students from lower socio-economic backgrounds may lack access to the necessary technology, which could hinder the widespread adoption of this method.

Beyond these challenges, the LivePBL DEEP Method offers several critical benefits through its practical applications. One of the most significant is personalised learning. By using Gen AI tools, the method can tailor educational content to individual students' needs, thereby supporting diverse learning preferences. This personalised approach ensures that every student, regardless of their background, can receive the education they deserve.

In addition to personalising learning, the method also plays a crucial role in bridging the gaps between inequality and equality. By focusing on creating co-designed learning experiences, the LivePBL DEEP Method actively works to address both sides of the inequality equation in education. This is done by engaging students, educators, and other stakeholders in designing and implementing solutions that promote equity.

Community engagement and co-learning are also at the heart of the LivePBL DEEP Method. The method encourages collaboration and community involvement, which are essential components of creating more inclusive educational environments. By fostering a sense of shared responsibility and mutual learning, the method helps to build stronger, more resilient educational communities.

In conclusion, the LivePBL DEEP Method, powered by Gen AI, represents a significant step forward in the quest for more inclusive and equitable education. By addressing both the theoretical and practical aspects of inequality, the method offers a comprehensive solution that can potentially transform the educational landscape. While challenges remain, particularly regarding scalability and ethics, the LivePBL DEEP Method provides a promising framework for bridging the gaps in education and ensuring that every student has the opportunity to succeed.