Dynamic Adaptive Pedagogical

Title: Enhancing Learning Through AI-Powered Pedagogy: A Dynamic Approach

Introduction:

In modern education, there exists a significant portion of learners who struggle to engage with traditional teaching methods. To address this challenge, we propose an innovative approach that leverages AI technologies within a dynamic pedagogical system. This system aims to personalize learning experiences and bridge the gap between desired learning outcomes and individual student capabilities.

AI System Integration:

Our approach integrates advanced AI systems, such as Microsoft Copilot, to analyze students' learning approaches and predict the types of learning materials required to meet their needs. By measuring the variance between desired and actual learning outcomes, the AI system dynamically adapts instructional content to ensure alignment with students' learning preferences and abilities.

Engagement and Support Mechanisms:

Through a hybrid asynchronous delivery model, our system provides continuous support to learners. A dedicated "Buddy-bot" acts as a companion, offering prompts and redirections when needed. This model ensures that learners receive timely assistance, even outside of traditional classroom settings, fostering a supportive learning environment.

SMART Learning Objectives:

Central to our approach is the reinforcement of SMART (Specific, Measurable, Achievable, Relevant, Time-bound) learning objectives. These objectives serve as the foundation for predictive modeling, guiding the development of personalized learning pathways tailored to each student's needs and aspirations.

Course Structure and Gamification:

The learning management system (LMS) is designed around branching scenarios and gamification elements to enhance engagement. Learners progress through various levels, each designed to challenge and build upon their cognitive skills according to Bloom's Taxonomy. Credits are earned at each milestone, celebrating achievements and motivating learners to advance further.

Alignment with Bloom's Taxonomy:

Our course structure aligns with Bloom's Taxonomy, progressing from foundational knowledge acquisition to the application of higher-order cognitive skills. Each level is carefully crafted to scaffold learning experiences, ensuring a gradual yet comprehensive mastery of key concepts and skills.

Conclusion:

By combining AI-driven predictive modeling with dynamic pedagogical approaches, our innovative system empowers learners to overcome traditional learning barriers. Through personalized support, engaging content, and scaffolded progression, we strive to cultivate a more inclusive and effective learning environment for all students.

It will assist learners who have difficulty engaging with traditional teaching approaches. It provides engagement through gamification elements and the available 24/7 'Chat-bot' to provide guidance and prompts for achieving the prescribed learning objectives. The system also measures the learners' responses and determines the type of learning format preferred by the learner and directs the learner within the LMS the necessary areas to reinforce knowledge and satisfy the learning objectives.

The LMS is based on current curricula with only clearer definition of the required learning outcomes. The LMS utilisies Microsoft Copilot which is readily available.

The LMS and Microsoft Copilot are Cloud based, accessed anywhere there is Internet coverage and run on Mobile (Cell) Phones and other mobile devices. 

The Institute for Equity in International Education currently provides online classes in Afghanistan to female students restricted from education. Our classes began 23rd November 2023 and very successful.

Initially our teachers of Afghan background based in Melbourne Australia, but intention to use female Afghan teachers based in Afghanistan.

We see no difficulty in duplication a similar system in the US, where after the roll-out of the system it can be operated in the US. The LMS course is hybrid-asynchronous, the learners can progress at their own speed using the directions and the prompts of the 'Chat-bot" but with the ability if the learner is having difficulties the system can contact a teacher immediately

We have been testing the ability of the LMS to recognize differences in responses between the desired learning objectives and the actual response and direction of the learner to the course material required to achieve the desired learning objective.

We have been testing the LMS to identify learners' preferred learning format (graphic, text)

It draws on current, easily accessed technology; our innovation is the use of this existing technology reducing development costs and introducing it into the market quickly.

The ability to automatically customize a learning program ideally suited to individual learners and maximizing their engagement and educational outcome is a game changer. It provides an easy avenue for the development of miro-credentials (or full-courses) that will be very attractive to prospective learners and education providers alike.

Once the system is available, with the use of AI the courses can be translated into any language and accessed via the internet anywhere in the world.

The core AI technology is based on Microsoft Copilot from GitHub OpenAI. the subdomains are Natural Language Processing (NLP) and Machine Learning (ML). We are using Copilot because of its availability and ease of use.

The use of Microsoft Copilot is well established and works very effectively. Our application of that software in the areas we are using it are also well established. Our specific use for personalized, dynamic, adaptive pedagogy is the application of known abilities of Microsoft Copilot. Our preliminary testing, albeit early, are very successful.

Our LMS is based on the pre-existing curricula in current use. We assume that the equity has previously been tested.

Our LMS is based on a series of models that represent different preferences in learning formats. The system monitors the learner's interaction and choice of model presented; the existing models can be adjusted to suit different learning approaches.

Initially we will be monitoring learner's progress very closely to ensure it satisfies our system requirements of engagement, progress and successful outcome. Of the initial learners some will be providing constant feedback to preempt any difficulties and address them quickly.

2 others and I are working on this program part-time.

The prototype will be aimed at the Grade 8 cohort, once we have successfully completed this Grade, we will be able to shape the courses for the younger learners.

The prototype can be completed with-in 6-months. Initial testing on learners will take 3-months.

Development for other Grades, 3-months.

In total 12-months.

To provide evidence we are on-track, we will provide access to our SharePoint intranet, where progress information is kept, and also provide access to the Microsoft Project's profile showing progress.

Our solution is online, available internationally via the internet and utilises Microsoft Copilot and a Learning Management System for the infrastructure, all are readily accessible.

The software already exists and is available by license from Microsoft.

Once the documentation has been created for Microsoft Copilot there are only administration costs which can be determined per learner.

Access to the necessary mobile phones required will need to be determined. By utilising our own SIM cards used we can use the volume of users to provide better internet access prices, either covered by the administration charge per learner or by philanthropic support.

Our approach is relatively easy to set up, with pre-existing software applications, which can be rolled out very effectively by utilising mobile phones for access and if necessary, finding ways to supplement those learners who may be having financial difficulties.

We do not have technical barriers; however, we need support to complete our research and development and the documentation with respect to defining Learning Outcomes in the existing curricula.