AI in Education as a methodology for enabling educational evidence-based practice

Evidence based practice (EBP) is of critical importance in Education where, increasingly, emphasis is placed on the need to equip teachers with an ability to independently generate evidence of their best practices in situ. Such contextualised evidence is seen as the key to in- forming educational practices more generally. One of the key challenges related to EBP lies in the paucity of methods that would allow educa- tional practitioners to generate evidence of their practices at a low-level of detail in a way that is inspectable and reproducible by others. This position paper focuses on the utility and relevance of AI methods of knowledge elicitation and knowledge representation as a means for sup- porting educational evidence-based practices through action research. AI offers methods whose service extends beyond building of ILEs and into real-world teaching practices, whereby teachers can acquire and apply computational design thinking needed to generate the evidence of in- terest. This opens a new dimension for AIEd as a field, i.e. one that demonstrates explicitly the continuing pertinence and a maturing reci- procity of the relationship between AI and Education

Accountability in human and artificial decision-making as the basis for diversity and educational inclusion

Accountability is an important dimension of decision-making in Human and Artificial Intelligence (AI). We argue that it is of fundamental importance to inclusion, diversity and fairness of both the AI-based and human-controlled interactions and any human-facing interventions aiming to change human development, behaviour and learning. Less well debated, however, is the nature and the role of biases that emerge from theoretical or empirical models that underpin AI algorithms and the interventions driven by such algorithms. While, the biases emerging from the theoretical and empirical models also affect human-controlled educational systems and interventions (e.g. hindsight and unconscious biases), the key mitigating difference between AI and human decision-making is that human decisions involve individual flexibility, context-relevant judgements, empathy, as well as complex moral judgements, missing from AI. In this chapter, we argue that our fascination with AI, which pre-dates the current craze by centuries, resides in its ability to act as a ‘mirror’ reflecting our current understandings of human intelligence. Such understandings also inevitably encapsulate the biases emerging from our intellectual and empirical limitations. We make a case for the need for diversity to mitigate against biases becoming inbuilt into systems (in both Education and AI) and, with reference to specific examples of AI approaches and applications, we outline one compelling future for inclusive and accountable AI and Educational research and practice

Becoming Better Versed: Towards Design of Popular Music-based Rhyming Game for Disadvantaged Youths

This study examines patterns of rhyme identification among English Language Learners (ELLs) towards the development of an educational game, JOLLY, intended to improve phonemic awareness among school-aged children in the Philippines. Leveraging on students’ intrinsic interest in Western popular music, we ask students to identify rhyming words from among the song lyrics. We find that the extent to which an English phoneme is similar to a Tagalog phoneme determines how likely it is to be identified. From these findings, we draw implications on how JOLLY’s underlying domain model can be structured with each song as a learning object and each learning object consisting of an inventory of phonemes to be mastered. We also recommend the use of open, possibly social, student models to help learners track their progress and that of their peers

Leveraging Non-cognitive Student Self-reports to Predict Learning Outcomes

Metacognitive competencies related to cognitive tasks have been shown to be a powerful predictor of learning. However, considerably less is known about the relationship between student’s metacognition related to non-cognitive dimensions, such as their affect or lifestyles, and academic performance. This paper presents a preliminary analysis of data gathered by Performance Learning Education (PL), with respect to students’ self-reports on non-cognitive dimensions as possible predictors of their academic outcomes. The results point to the predictive potential of such self-reports, to the importance of students exercising their self-understanding during learning, and to the potentially critical role of incorporating such student’s self-reports in learner modelling

Towards the Development of a Computer-based Game for Phonemic Awareness

In this paper, we discuss some of the results of a participatory design workshop used to elicit design guidelines for an education game for phonemic awareness intended for use by disadvantaged students. Using a grounded theory approach, we analyze facilitators’ observations from the workshop and related findings to well-established game design guidelines. We were able to align facilitators’ observations with these guidelines in order to prescribe ways to support student participation, mitigate student disengagement, support various team roles and dynamics, and accommodate a variety of game play strategies