International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

As artificial intelligence (AI) is integrated into education and workforce development, the ability to engage reflexively with technology may represent an emerging metacognitive skill. This paper conceptualizes reflexivity as an intentional, cyclical process of recognizing assumptions, reflecting on human-AI interactions, and responding with ethical discernment. Drawing from counselor education and supervision models that emphasize self-awareness and metacognitive reflection, the paper proposes a conceptual framework for cultivating ethical reflexivity in AI-augmented learning and decision-making environments. Through conceptual analysis and integration of the Triadic Learning Alliance (TLA) model, the framework identifies potential strategies for professionals and students to monitor cognitive biases, critically examine algorithmic authority, and maintain human empathy and accountability when collaborating with AI systems. The discussion situates reflexivity within broader interpersonal and metacognitive competencies that may support lifelong learning and ethical reasoning in a data-driven world. This paper argues that reflexivity may function as a foundational metacognitive process for ethical human-AI collaboration in AI-augmented learning environments. A link to a video of Jennifer Young's presentation can be found below in the Additional Files section.

This case study reports a full redesign of an undergraduate Intercultural Communication seminar in Toronto, repositioning the course to foreground power skills as durable differentiators in an AI-mediated economy. The redesign shifts an asynchronous distance format into a seminar-based model with weekly applied learning, scaffolded assessments, and a four-week COIL partnership with a Turkish university to create authentic intercultural collaboration under real constraints. Students practice creativity, curiosity, critical thinking, ethical reasoning, decision-making, verbal and nonverbal communication, cross-cultural competence, and self-reflexivity, while also learning to use AI tools in ways that remain accountable to disciplinary standards. Assignments include debates, peer assessment, collaborative cross-cultural projects, and critical autoethnographic narratives that surface bias, ethnocentrism, conflict resolution, and face needs. The session offers a replicable blueprint for humanities-led pedagogy that develops empathetic communication and ethical judgment while still preparing students for hybrid human–AI workplaces. A link to a video of Nitin Deckha's presentation can be found below in the Additional Files section.

In recent years, the U.S. legal system has seen an increase of legal filings using fictitious court cases or legal propositions generated by artificial intelligence (AI) (Stokel-Walker, 2026). Rules of professional responsibility require that lawyers review filings in which AI was used to ensure their accuracy (Missouri Bar, Office of Legal Ethics Counsel, 2024, Opinion No. 2024‑11) Despite this mandate, filings with fictitious cases and incorrect statements of law are being filed. Besides posing a threat to the parties to an action, such filings may set an unwarranted precedent for future cases. They also tie up court resources searching for nonexistent law or unfounded propositions of law and erode public trust. Further, an AI generated summary of a legitimate court case presents a risk of being misleading as it may fail to capture a true understanding of the case. In this study, undergraduate criminal justice college students provided human review of AI generated legal material. The students were asked to complete a typical undergraduate criminal law and procedure class assignment. At random, students were either given an excerpt from a real legal case decision or an AI generated summary of the same. Students were then asked to read the excerpt or AI summary and answer the same questions based upon their reading. Not surprisingly, students using the AI summary were not able to correctly or fully answer the questions, as the AI summary did not capture the nuances or depth of the legal case that may be found by a full reading of the case excerpt.

AI-driven audio tools are reshaping musical creation, yet the most consequential shift is not automation of artistry but reconfiguration of collaboration, metacognition, and quality control within large ensembles. This paper examines how generative audio models, vocal synthesis platforms, and stem-splitting technologies can function as creative partners in music arranging and composition workflows, particularly in musical contexts where artistic coordination and interpretive judgment remain paramount. We position AI in music creation as analogous to the calculator in mathematics: widely available, efficiency-enhancing, and therefore unavoidable, while still requiring disciplined human oversight to preserve intent, style, and accountability. The paper surveys practical implementation and limitations, including iterative ideation, timbral experimentation, and rapid prototyping, while also addressing ethical issues of access, authorship, and disclosure. Readers will leave with a realistic understanding about how AI can augment creative craft without displacing professional expertise. A link to a video presentation related to this paper can be found below in the Additional Files section.

Artificial intelligence (AI) comprises not only models, but full socio-technical systems involving data pipelines, instrumentation, human-machine interfaces, deployment architectures, and organizational processes for design, monitoring, and evaluation. Using a systems-oriented analytical framework, this paper argues that despite accelerating advances in AI capabilities, human capital remains the enduring and dominant system constraint. Human interfaces define throughput limits in areas such as prompt engineering, data-stream curation, adjudication of model outputs, and the orchestration of hybrid automation workflows including robotics, scraping, and digitization. Synthesizing emerging research across human-AI interaction, machine-learning lifecycle management, organizational adoption, and adult learning theory, we present a socio-technical evaluation model that characterizes key human factors—trust calibration, output-quality sensemaking, expertise depth, feedback latency, cognitive load, and metacognitive skill development—as performance-shaping mechanisms within AI-enabled systems. We show how organizational structures, bias susceptibility, retraining constraints, and interface design co-determine system stability, error propagation, and optimization ceilings. Finally, we propose key design principles for workforce development grounded in these systems design principles, constraint reduction, and continuous evaluation. This perspective reframes humans not as passive users, but as core system components whose competencies, limitations, and adaptive capacities constrain the performance envelope of optimized AI systems. A link to a video related to this presentation can be found below in the Additional Files section.

Metacognition is often presented as a response to generative AI’s disruption of teaching and learning, yet the term has become too generalized to guide practice. In AI-mediated environments, asking students merely to “reflect on your thinking” is insufficient. Because AI tools can redistribute cognitive labor, their educational value depends on how students use them and whether that use supports disciplinary forms of reasoning. This essay argues that metacognition must be understood as disciplinary infrastructure: students cannot effectively monitor their thinking without understanding the epistemological and ontological demands of the field in which they are working. Classrooms therefore become sites where student frameworks interact with more discipline-grounded instructional frameworks. Teachers must balance immersion and friction, enabling students to enter the flow of inquiry while introducing strategic pauses that make disciplinary expectations visible. Tina Austin’s UnBlooms Framework offers one model through “metacognitive checkpoints,” where students evaluate whether AI is helping or hindering their learning. These checkpoints ask students to discern when AI supports a discipline-responsive habit of mind and when they should resist offloading and complete a task themselves. The essay reframes metacognition as concrete, discipline-sensitive, and grounded in judgment within AI-mediated learning. A link to a video related to this presentation can be found below in the Additional Files section.

Telling students to “reflect on their own thinking” has become insufficient in AI-mediated learning environments. When students are rewarded for polished outputs, cognitive offloading to AI tools becomes rational, and traditional snapshot assessments (single-moment evaluations of task completion) produce false signals about whether durable learning has occurred. This problem is sharpened by the broader shift in AI learning tools toward Socratic tutors, study modes, Khan Academy's Khanmigo, and agentic systems that can shape the learner’s process over time. Lodge and Loble (2026) distinguish beneficial cognitive offloading, which frees working memory for intrinsic learning, from detrimental outsourcing, which bypasses the cognitive work that builds durable understanding. This paper introduces two behavioral instruments designed to make that distinction observable in classroom contexts. The UnBlooms™ Metacognitive Awareness Scale (MAS) is a five-level developmental taxonomy that operationalizes evaluative judgment as instructor-scored evidence. The UnBlooms™ Discernment Rate (UDR) is a classroom-level metric tracking the proportion of AI outputs a learner interrogates, challenges, or revises rather than accepting at face value. Together, the MAS and UDR shift assessment from product snapshots toward longitudinal interaction trajectories: the sequence of decisions, revisions, and resistances through which metacognitive development becomes visible. The paper situates these tools in the cognitive offloading literature, compares them to the recently validated Metacognitive Laziness Scale (Dizon et al., 2026), and proposes testable hypotheses linking MAS, UDR, and metacognitive laziness. It also identifies the validation work required before these instruments can be treated as psychometrically established measures.

The relationship between teacher quality and student learning achievement remains complex and somewhat inconsistent across the literature. For example, Roorda et al. found that the association between teacher–student relationship quality and student achievement was positive in most studies, but negative in others. Similarly, Yang and Kaiser reported that teaching quality showed significantly positive, non-significant, and even significantly negative relationships with student learning outcomes. In the Cambodian context, Chhin and Tabata examined the relationship between teacher quality and student achievement and found that teacher economic status, job satisfaction, and teaching experience significantly influenced student learning outcomes; however, these variables explained only 20 percent of the variance in student achievement. These mixed findings highlight the importance of further investigating the factors underlying the relationship between teacher quality and student learning outcomes. Therefore, this study examines the relationship between teacher quality and students’ academic achievement in reading and mathematics among Grade 5 primary school students in Cambodia. Specifically, the study focuses on three teacher-related variables: teaching experience, educational background, and pre-service training. The analysis is guided by the education production function model to examine how these teacher characteristics influence students’ learning achievement. This study employs a quantitative research design using data from the Southeast Asia Primary Learning Metrics conducted in 2019 across six Southeast Asian developing countries, which assessed Grade 5 students’ performance in mathematics, reading, and writing. The findings indicate that students’ socio-economic status (SES), non-repeater status, and attendance at urban schools are positively and significantly associated with both mathematics and reading achievement. In contrast, male students show lower performance than female students in both subjects. Parents’ educational attainment demonstrates a partial association with students’ academic performance. Regarding teacher characteristics, students taught by teachers with pre-service training perform significantly better than those taught by teachers without such training. Similarly, compared with teachers who completed only primary education, teachers with lower secondary, upper secondary, bachelor’s, master’s, or doctoral qualifications are more likely to positively influence students’ mathematics and reading achievement. However, teaching experience does not show a statistically significant relationship with student achievement in either subject, regardless of whether teachers have 1–5 years or more than 6 years of teaching experience. This suggests that teaching experience alone may not be sufficient to improve student learning outcomes. When comparing rural and urban contexts, the results show that teachers with pre-service training are positively associated with students’ mathematics achievement in rural schools only. Meanwhile, teachers with higher educational qualifications demonstrate positive associations with student achievement in both rural and urban schools. However, teaching experience remains statistically insignificant across both contexts. Future research should further explore how school and household environments interact to support children’s academic development. In addition, the use of richer longitudinal or panel data would allow the application of fixed-effects models, which may provide more robust and reliable estimates of the relationship between teacher quality and student learning achievement.