Use of Artificial Intelligence in educational portals and OER repositories Irene-Angelica Chounta Senior Researcher Centre for Educational Technology, University of Tartu chounta@ut.ee

Objective To provide an overview of AI and ML practices To discuss benefits, challenges and risks To explore actions for fostering the adoption of AI and ML

Artificial Intelligence and Machine Learning Artificial Intelligence (AI): technologies that allow machines to act and take decisions imitating human intelligence (McCarthy, 1998) Machine Learning (ML): algorithms that teach machines how to perform specific tasks through experimentation (Michalski, Carbonell, & Mitchell, 2013).

Artificial Intelligence and Machine Learning Intelligent Tutoring Systems (ITS) computer-assisted instruction systems (CAI) 1970s Constructionism (Papert, 1980) 1980s 1990s Online Learning (eLearning) 2000s MOOCs Learning Analytics 2010s OER, educational portals Artificial Intelligence (AI): technologies that allow machines to act and take decisions imitating human intelligence (McCarthy, 1998) Machine Learning (ML): algorithms that teach machines how to perform specific tasks through experimentation (Michalski, Carbonell, & Mitchell, 2013).

AI in education (incl. OER & portals) today Assessment Recommendations Personalization and adaptation Learners Instructors Other stakeholders (e.g. course designers, policy makers, content authors) Intelligent Tutoring Systems Online Learning Platforms (e.g. LMS, MOOCS) OER portals and repositories

Assessment Predictive student models (ITS) Social network analysis (MOOCs) Learning analytics (LMS, educational portals)

Recommendations Knowledge-based (ontology) recommendations Content-based recommendations Collaborative filtering Social-network recommendations Hybrid recommendations

Recommendations Knowledge-based (ontology) recommendations Content-based recommendations Collaborative filtering Social-network recommendations Hybrid recommendations

Personalization, adaptation Feedback Automated or semi-automated textual descriptions (prompts) Visualizations / Adaptive dashboards Learning Materials Materials’ adaptation focused on localization / quality Adaptation/personalization focused on users’ needs

Examples of use Open Learning Initiative @ CMU eDidaktikum Knewton OER portal and Open Online Learning platform Cognitive ML student models for performance assessment Authoring tools for instructors Open Learning Initiative @ CMU eDidaktikum Knewton NextLab / GoLab IBM Watson + Edmodo

Examples of use Open Learning Initiative @ CMU eDidaktikum Knewton NextLab / GoLab IBM Watson + Edmodo Educational portal for teachers’ training Competence models for real-time tracking of skills acquisition Work-in-progress

Examples of use Open Learning Initiative @ CMU eDidaktikum Knewton NextLab / GoLab IBM Watson + Edmodo Adaptive learning platform Machine learning models to assess students’ mastery levels

Examples of use Open Learning Initiative @ CMU eDidaktikum Knewton NextLab / GoLab IBM Watson + Edmodo Online labs Machine learning analytics to inform teachers about students’ progress Has been successfully integrated in K-12 classrooms in Europe and internationally

Examples of use Open Learning Initiative @ CMU eDidaktikum Knewton NextLab / GoLab IBM Watson + Edmodo AI personalized agents for identifying learning gaps NLP, machine learning student models New partnership

Adopting AI: Potential benefits Allows adaptation and personalization of materials, learning environments and learning process Provides tools for stakeholders to retrieve appropriate resources Scaffolds learning through monitoring, mirroring and guiding

Adopting AI: From research to practice New technologies take too long to make it to the classroom – if at all We lack a common view on shared technological challenges Users’ expectations about technology change rapidly Stakeholders lack a common understanding about computational tools, benefits and pitfalls

Adopting AI: Challenges Pedagogical challenges Technical challenges Privacy challenges

Adopting AI: Challenges Pedagogical challenges Technical challenges Privacy challenges New roles for teachers and students The social aspect of learning Quality control of material Evidence-based approaches

Adopting AI: Challenges Pedagogical challenges Technical challenges Privacy challenges User-centered designs over one- size-fits-all Cost vs. efficiency /effectiveness tradeoff Openness and accessibility, privacy and security

Adopting AI: Challenges Pedagogical challenges Technical challenges Privacy challenges Personalization over privacy dilemma Just because its “there”, doesn’t mean its ok to use it Informed consent

Conclusion Despite the promising benefits, adoption of new technologies is slow (and painful….) One small step at a time: Supporting local structures and directives Facilitate transition through communication, openness, experimentation

The next day Steps to promote AI integration: to help stakeholders familiarize with AI and ML research and practice; to carry out long-term initiatives for demonstrating AI technologies in the field to involve stakeholders when designing cutting-edge computational tools

A new learning paradigm: BYOR A socio-technical approach for AI-supported OER portals to promote 21st century skills: - students are encouraged to retrieve, use and assess OER resources; - teachers create conditions for enabling a new learning approach; - a crowdsourcing infrastructure to ensure quality control and sharing of resources

Discussion Happy to answer your questions :) chounta@ut.ee