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AI for Education

The AI for Education project explores how artificial intelligence and digital technologies can enhance teaching, learning, and educational management within the Sri Lankan context. The project addresses both pedagogical capacity building and the development of intelligent tools that support efficient academic processes in higher education.
One component of the project focuses on teacher professional development in digital and AI-enabled education. Through interactive workshops and hands-on training sessions, school teachers were introduced to learning management systems, automated assessments, digital content creation, and emerging AI-powered educational tools. Findings from this pilot programme highlight that while teachers show strong interest in integrating technology into their teaching practices, foundational digital literacy remains essential. The programme demonstrates that targeted training initiatives can significantly improve teachers’ confidence in designing technology-enhanced learning activities and supporting diverse learner needs.
Another key research direction focuses on AI-driven solutions for academic assessment management in university settings. In a large undergraduate software engineering project course, algorithms were developed to automatically compose expert evaluation panels for synoptic project assessments based on project technology profiles and evaluator expertise. This automated approach successfully matched the majority of student projects with appropriate expert panels, significantly reducing manual scheduling complexity. Together, these efforts demonstrate how AI-based methods can strengthen educational capacity, improve teaching practices, and streamline assessment processes.The Common Sense Reasoning project focuses on advancing culturally grounded evaluation and knowledge representation for language-based artificial intelligence systems. Commonsense reasoning is a critical capability for intelligent systems, yet existing benchmarks and resources often overlook cultural and linguistic diversity. This project addresses these limitations by developing datasets, evaluation frameworks, and knowledge resources that capture everyday reasoning across languages and cultural contexts.
A major component of the project contributes to the development and analysis of a large multilingual benchmark designed to evaluate commonsense reasoning across more than one hundred languages. Constructed through a global collaborative effort involving hundreds of researchers, the benchmark includes culturally specific scenarios referencing local customs, foods, and traditions. Experimental findings reveal that while modern language models perform well overall, their performance drops significantly for lower-resource languages, highlighting the need for more inclusive evaluation resources.
Complementing this effort, the project also introduces a culturally grounded commonsense reasoning dataset tailored to the Sinhala language, incorporating locally relevant examples reflecting Sri Lankan contexts. In addition, earlier work explores methods for automatically generating commonsense knowledge bases by extracting semantic relationships from large text corpora using rule-based and learning-based approaches. Together, these contributions aim to strengthen culturally aware commonsense reasoning capabilities in intelligent systems and improve evaluation and resource availability for underrepresented languages.

Objectives:

• Enhance digital and artificial intelligence literacy among educators by designing and delivering training programmes that equip teachers with practical skills for integrating technology and AI-powered tools into classroom teaching.
• Develop intelligent tools to support educational administration and assessment, including automated systems that assist in tasks such as evaluator selection, scheduling, and management of project-based assessments.
• Improve the effectiveness and scalability of technology-enhanced learning environments by exploring the use of learning management systems, automated assessments, and interactive digital teaching tools.
• Promote evidence-based adoption of artificial intelligence in education by studying the impact of AI-driven solutions on teaching practices, student learning experiences, and institutional academic processes.

Keywords: Education | Machine Learning / Deep Learning | Technology in Education | Computer Science Education |

Publications

Team

External Collaborators: | Buddhika Karunarathne | Vishaka Nanayakkara | Thanuja Ambegoda | Shahani Markus | Shehan Perera |