Periodic Reporting for period 1 - augMENTOR (Augmented Intelligence for Pedagogically Sustained Training and Education)
Okres sprawozdawczy: 2023-01-01 do 2023-12-31
augMENTOR aims to meet the impacts of the Horizon Europe Strategic Plan by contributing to the “Social and economic resilience and sustainability are strengthened through a better understanding of the social, ethical, political and economic impacts of drivers of change (such as technology, globalization, demographics, mobility and migration) and their interplay.”
The project keeps a strategic and ethical balance between the pedagogical and technological dimensions of teaching and learning. It leverages advancements in the fields of Pedagogical Design, Creative Pedagogy, Explainable Artificial Intelligence, Knowledge Representation and Reasoning for instructional purposes. Our team develops a novel pedagogical framework that promotes both basic skills and 21st century competencies by integrating emerging technologies in contemporary teaching methodologies while offering a dedicated assessment strategy that shall be seamlessly integrated in the proposed solution.
Our framework is supported by an open access AI-boosted toolkit that builds on the strengths of machine learning and learning analytics to provide different types of stakeholders with explainable recommendations for enhancing learner’s performance and assessment by considering each learner’s unique traits, requirements, skills as well as preferences.
The overall approach foreseen is considered innovative, as it leverages both structured and unstructured data to capture the stakeholders’ learning paths and offer meaningful insights and recommendations that help optimize the learning process. Thus, augMENTOR will trigger constructive reflection in both tutoring and pedagogical policy making.
The foreseen solution will be deployed and thoroughly validated in four different pilot settings which represent diverse educational and training settings to define best practices.
The project keeps a strategic and ethical balance between the pedagogical and technological dimensions of teaching and learning. It leverages advancements in the fields of Pedagogical Design, Creative Pedagogy, Explainable Artificial Intelligence, Knowledge Representation and Reasoning for instructional purposes. Our team develops a novel pedagogical framework that promotes both basic skills and 21st century competencies by integrating emerging technologies in contemporary teaching methodologies while offering a dedicated assessment strategy that shall be seamlessly integrated in the proposed solution.
Our framework is supported by an open access AI-boosted toolkit that builds on the strengths of machine learning and learning analytics to provide different types of stakeholders with explainable recommendations for enhancing learner’s performance and assessment by considering each learner’s unique traits, requirements, skills as well as preferences.
The overall approach foreseen is considered innovative, as it leverages both structured and unstructured data to capture the stakeholders’ learning paths and offer meaningful insights and recommendations that help optimize the learning process. Thus, augMENTOR will trigger constructive reflection in both tutoring and pedagogical policy making.
The foreseen solution will be deployed and thoroughly validated in four different pilot settings which represent diverse educational and training settings to define best practices.
Our team focused both on eliciting user requirements as well as mapping the research landscape since the early stages of the project.
Systematic literature reviews were performed on Artificial Intelligence in education focusing on:
- Artificial Intelligence algorithms and technologies currently used in education and educational research
- How Emerging Technologies are integrated into different aspects of the teaching and learning process
- Approaches elaborating digitized educational resources to develop and foster 21st century competencies.
Aiming to operate in a Responsible Research and Innovation (RRI) context, the team also worked on safeguarding these principles in its work, duly prioritizing the judicious integration of technology in a socially responsible manner.
To actively involve users in the design of the augMENTOR solution by gaining an understanding of present-day education and training practices with a particular emphasis on data-intensive activities, we reached out to the teaching and training community and invited practitioners to participate in our project’s elicitation of user requirements activities. Τhe elicitation of functional and non-functional requirements played a key role in the design of the augMENTOR reference architecture for which we followed an iterative approach; a cyclic process of prototyping, testing, analyzing, and refining the reference architecture.
On technical level data resources and orchestration design as well as collection of the piloting data features were performed. Moreover application of unsupervised feature selection and clustering techniques for the deployment of the augMENTOR profiles was conducted.
Pilot preparation has also started ahead of schedule. Pilot have developed their courses and tailored the assessment strategy to their needs. They set up recruitment plans and are ready to test their courses aiming to refine them before pilot implementation.
In addition, the project team set in place all the necessary guidelines, documents and mechanisms to ensure legal and ethical compliance across project activities throughout the lifetime of the project. An external ethics advisory board has also been appointed as well as an ethics manager to hold the ethical oversight of the project.
Finally, the team has outlined the next steps towards delivering an effective augMENTOR solution with all the foreseen functionalities including.
Systematic literature reviews were performed on Artificial Intelligence in education focusing on:
- Artificial Intelligence algorithms and technologies currently used in education and educational research
- How Emerging Technologies are integrated into different aspects of the teaching and learning process
- Approaches elaborating digitized educational resources to develop and foster 21st century competencies.
Aiming to operate in a Responsible Research and Innovation (RRI) context, the team also worked on safeguarding these principles in its work, duly prioritizing the judicious integration of technology in a socially responsible manner.
To actively involve users in the design of the augMENTOR solution by gaining an understanding of present-day education and training practices with a particular emphasis on data-intensive activities, we reached out to the teaching and training community and invited practitioners to participate in our project’s elicitation of user requirements activities. Τhe elicitation of functional and non-functional requirements played a key role in the design of the augMENTOR reference architecture for which we followed an iterative approach; a cyclic process of prototyping, testing, analyzing, and refining the reference architecture.
On technical level data resources and orchestration design as well as collection of the piloting data features were performed. Moreover application of unsupervised feature selection and clustering techniques for the deployment of the augMENTOR profiles was conducted.
Pilot preparation has also started ahead of schedule. Pilot have developed their courses and tailored the assessment strategy to their needs. They set up recruitment plans and are ready to test their courses aiming to refine them before pilot implementation.
In addition, the project team set in place all the necessary guidelines, documents and mechanisms to ensure legal and ethical compliance across project activities throughout the lifetime of the project. An external ethics advisory board has also been appointed as well as an ethics manager to hold the ethical oversight of the project.
Finally, the team has outlined the next steps towards delivering an effective augMENTOR solution with all the foreseen functionalities including.
During its first year of operations, augMENTOR has made substantial progress in the following fields:
- Elicitation of user requirements (operational and functional) as well as technical requirements that led to the development of system specifications and to the reference architecture design of the augMENTOR solution
- Design of the augMENTOR pedagogical framework based on the related literature review and the elicited user requirements.
- Integration of the principles of creative pedagogy in the augMENTOR framework and solution through the development of a state-of-the-art paradigm on creativity in AI and a methodology for the assessment of creativity in the pilot learning activities.
- Experimentation with machine learning pipelines for knowledge discovery. Two dedicated functionalities were developed, one for ad hoc requesting and retrieving information/ feedback for specific learner(s) regarding their learning progress and a second one for receiving recommendations on a learner’s performance.
- Identification of Key Exploitable Results bundles and demonstration to related stakeholders towards ensuring further exploitation of the project’s assets and establishing a commercialization and bridge-to-market strategy.
- Elicitation of user requirements (operational and functional) as well as technical requirements that led to the development of system specifications and to the reference architecture design of the augMENTOR solution
- Design of the augMENTOR pedagogical framework based on the related literature review and the elicited user requirements.
- Integration of the principles of creative pedagogy in the augMENTOR framework and solution through the development of a state-of-the-art paradigm on creativity in AI and a methodology for the assessment of creativity in the pilot learning activities.
- Experimentation with machine learning pipelines for knowledge discovery. Two dedicated functionalities were developed, one for ad hoc requesting and retrieving information/ feedback for specific learner(s) regarding their learning progress and a second one for receiving recommendations on a learner’s performance.
- Identification of Key Exploitable Results bundles and demonstration to related stakeholders towards ensuring further exploitation of the project’s assets and establishing a commercialization and bridge-to-market strategy.