In science, technology, engineering, arts, and mathematics (STEAM) education, artificial intelligence (AI) analytics are useful as educational scaffolds to educe (draw out) the students’ AI-Thinking skills in the form of AI-assisted human-centric reasoning for the development of knowledge and competencies. This paper demonstrates how STEAM learners, rather than computer scientists, can use AI to predictively simulate how concrete mixture inputs might affect the output of compressive strength under different conditions (e.g., lack of water and/or cement, or different concrete compressive strengths required for art creations). To help STEAM learners envision how AI can assist them in human-centric reasoning, two AI-based approaches will be illustrated: first, a Naïve Bayes approach for supervised machine-learning of the dataset, which assumes no direct relations between the mixture components; and second, a semi-supervised Bayesian approach to machine-learn the same dataset for possible relations between the mixture components. These AI-based approaches enable controlled experiments to be conducted in-silico, where selected parameters could be held constant, while others could be changed to simulate hypothetical “what-if” scenarios. In applying AI to think discursively, AI-Thinking can be educed from the STEAM learners, thereby improving their AI literacy, which in turn enables them to ask better questions to solve problems.

Identifying educational competencies for the 21st workplace is driven by the need to mitigate disparities between classroom learning and the requirements of workplace environments. Multiple descriptors of desired 21st century skill sets have been identified through various wide-scale studies (e.g., International Commission on Education for the 21st Century) and consistently within the context of science, technology, engineering, and mathematics (STEM) learning, the ability to problem solve, particularly complex problem-solving, remains a crucial competency. In this paper, we look at how current contemporary spaces such as the immensely popular, massively multiplayer online role-playing game(MMORPG), World of Warcraft, (WoW) afford problem-solving skill acquisition in the context of Singaporean youth learners. Given that WoW exists as a contextual space with an overarching narrativized problem to be solved, our investigation focused on two important related constructs that underpin learners’ problem-solving trajectory—learning and identity becoming within contemporary domains of technology learning. We present findings of an ethnographic investigation of one youth gamer within the affinity spaces of WoW. Moving away from traditional mentalistic construals of problem-solving, our findings indicate that problem-solving within WoW may be characterized by a triadic-D model of domain, disquisitional, and discursive practices within self, social, and structural dialectics. Theoretical considerations for broadening the understanding of a situated and embodied notion of problem-solving and identity becoming within STEM learning are proposed.

Artificial intelligence-enabled adaptive learning systems (AI-ALS) are increasingly being deployed in education to enhance the learning needs of students. However, educational stakeholders are required by policy-makers to conduct an independent evaluation of the AI-ALS using a small sample size in a pilot study, before that AI-ALS can be approved for large-scale deployment. Beyond simply believing in the information provided by the AI-ALS supplier, there arises a need for educational stakeholders to independently understand the motif of the pedagogical characteristics that underlie the AI-ALS. Laudable efforts were made by researchers to engender frameworks for the evaluation of AI-ALS. Nevertheless, those highly technical techniques often require advanced mathematical knowledge or computer programming skills. There remains a dearth in the extant literature for a more intuitive way for educational stakeholders—rather than computer scientists—to carry out the independent evaluation of an AI-ALS to understand how it could provide opportunities to educe the problem-solving abilities of the students so that they can successfully learn the subject matter. This paper proffers an approach for educational stakeholders to employ Bayesian networks to simulate predictive hypothetical scenarios with controllable parameters to better inform them about the suitability of the AI-ALS for the students.