Journal Article
22 June 2026
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- Journal Article22 June 2026
AI as a Creative Collaborator in Music: Exploring Human-Centered Innovation in Large Musical Contexts
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.
Current Articles
- Journal Article22 June 2026
Reflexivity as a Metacognitive Skill: A Conceptual Triadic Learning Alliance Framework for Ethical Human-AI Collaboration in Education
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. - Journal Article22 June 2026
Redesigning an Intercultural Communication Course to Build Power Skills for an AI-Augmented Global Economy
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. - Journal Article22 June 2026
Law or Flaw: A Double – Blind Study Comparing Student Comprehension of Real and AI Generated Legal Case Briefs
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. - Journal Article22 June 2026
AI as a Creative Collaborator in Music: Exploring Human-Centered Innovation in Large Musical Contexts
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. - Journal Article22 June 2026
Optimizing Human Capital in AI-Enabled Architectures: A Systems Constraint and Capability Analysis
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. - Journal Article22 June 2026
Metacognition as Disciplinary Infrastructure in AI-Mediated Learning
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. - Journal Article22 June 2026
Toward a Metric for Disciplinary Learning in the Age of AI: The UnBlooms™ Metacognitive Awareness Scale and Discernment Rate in AI-Mediated Learning
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. - Journal Article22 June 2026
An Analysis of Teacher Quality and Primary School Students’ Learning Achievements in Cambodia
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. - Journal Article22 June 2026
Visionarium, June 2026, Full Issue
Most Popular Articles
- Journal Article3 June 2024
AI Integration in Cultural Heritage Conservation – Ethical Considerations and the Human Imperative
The integration of artificial intelligence (AI) into the conservation of cultural heritage marks a significant transformation in preservation methodologies, heralding both innovative solutions and complex ethical dilemmas. This article undertakes a comprehensive examination of the multifaceted role AI plays in the conservation and restoration of cultural artifacts, buildings, and sites, underscoring the irreplaceable value of human skills and ethical judgment in this domain. Through an analysis of current research, case studies, and insights from professionals in the field, the paper elucidates how AI technologies—encompassing machine learning algorithms, digital twinning, and predictive maintenance—can enhance the accuracy and efficiency of conservation efforts. However, it simultaneously addresses the ethical quandaries these technologies engender, including the risks of inauthentic restoration, the perpetuation of biases, and the erosion of cultural sensitivity. By advocating for a balanced approach that leverages AI's capabilities while safeguarding against its potential pitfalls, the study calls for establishing interdisciplinary governance frameworks and ethical guidelines to navigate the intricate interplay between technological advancement and cultural heritage preservation. Ultimately, the paper posits that the integration of AI into cultural heritage conservation necessitates a symbiotic relationship between technological innovation and the nuanced, irreplaceable human element, ensuring that efforts in preservation are as ethically informed as they are technologically advanced. - Journal Article3 June 2024
Embracing the Irreplaceable: The Role of Neurodiversity in Cultivating Human-AI Symbiosis in Education
This study investigates the indispensable role of human skills—such as empathy, ethical judgment, and nuanced understanding—in the development and application of artificial intelligence (AI) within higher education, highlighting the unique contributions of neurodivergent perspectives in creating a symbiotic human-AI relationship. Drawing upon research that evidences the superior performance of diverse teams in creativity and innovation, the paper argues for the integration of neurodiversity into AI development as a means to address the philosophy of 'fearing the Other,' thereby mitigating biases and fostering ethical AI interactions. The technology sector's adoption of Diversity, Equity, and Inclusion (DEI) programs, including biopsychosocial interventions and environmental adaptations to support the neurodivergent workforce, serves as a model for higher education. By leveraging the estimated 15-20% of the global population which is neurodivergent, this approach not only aims to alleviate the employment disparities faced by neurodivergent individuals, but also enriches the ethical and innovative capacities of educational AI systems. This concise analysis advocates for an educational technology landscape that not only replicates human intelligence but also embodies human values, thanks to the invaluable contributions of the neurodivergent community. - Journal Article27 July 2023
FinChain: Adaptation of Blockchain Technology in Finance and Business - An Ethical Analysis of Applications, Challenges, Issues and Solutions
Blockchain Technology is a distributed database technology that has emerged as a ground-breaking technology with several possible solutions to critical applications, say from supply chain management, agribusiness, marketing to healthcare industry including internet of medical things. Although it started as a digital coin (popularly known as bitcoin), it is slowly influencing business, marketing policy and society. We have presented an in-depth study and ethical analysis of how blockchain is applied over the economic and financial sector including banks, credit unions and other retail giants. During our research, we have also investigated how blockchain technology can affect financial institutions around the world and businesses including large and small businesses. Our contributions included the following: (i) classifying blockchain models and architecture for finance and business markets (ii) analyzing recent and relevant works for finance applications and business solutions using blockchain (iii) discussing the advantages of using blockchain technology in financial institutions like banks, government firms. education sectors (iv) pointing out challenges and issues of blockchain technology for finance and business organizations (v) summarizing future research on integration and adaptation of blockchain technology along with C2C, B2C, and B2B with finance and recommendations for improvement. - Journal Article27 July 2023
Rebranding Originality for the Age of AI
"Originality" has been a longstanding focal point within the college classroom, with students being encouraged to embrace creativity and boldness. The traditional view of originality, relying solely on one's wit and imagination, has lost its effectiveness in the present era. The concept of learning has undergone a significant transformation, no longer resembling the isolated ivory tower of the past where individuals would immerse themselves in books, hoping to be inspired. Instead, modern learning has become more social and collaborative. Students compare and contrast class material with online resources, engaging in conversations, both in person and virtually, to solidify their understanding. The author of the presentation contends that the future of higher education lies in collaborative originality. Collaboration goes beyond the mere sharing of ideas; it serves as a means of generating innovative concepts, thriving in the dissolution of traditional boundaries. The delineation between humans and machines, disciplines, and formal and informal learning has become increasingly blurred. In this context, modern originality emerges as a collaborative and interdisciplinary process. The advent of AI, notably exemplified by technologies like ChatGPT and Hyperwrite, has further accelerated this trend. Utilizing prompt engineering, individuals can seamlessly collaborate with virtual assistants to foster new ideas, revolutionizing the conventional notion of "originality." - Journal Article7 July 2025
Bridging Tradition and Technology: AI as a Catalyst for Heritage Preservation and Humanities Research
It is an empirical study examining how AI can be the driving force for cultural heritage and humanities conservation. As the AI tools that digitize, analyze, and conserve cultural resources have become in recent years, they have led to digital transformations in machine learning, digital twinning, and natural language processing. These technologies answer the industry's most challenging problems, such as the loss of historical material, access restrictions to heritage sites, and the labor-intensive nature of traditional conservation techniques. In implementing AI in heritage, institutions can document more accurately, keep records predictively, and offer more interesting public experiences via rich digital media. Nevertheless, the effects of AI on cultural heritage do not stop there. It is an article about AI's shift to humanities research practices. Humanities researchers have long been guided by interpretive, qualitative methods of historical analysis: texts, artifacts, and cultural histories. Artificial intelligence tools, in contrast, bring discipline into the data – looking for patterns and relationships humans cannot. Natural language processing systems, for example, can rapidly and accurately transcribe and interpret vast archive texts – unearthing unseen truths and forgotten voices from long ago. Just as radically, AI-powered image recognition software allows scientists to pick out finer features in paintings, excavations, and manuscripts, allowing for novel interpretations and insights. The study also points out that interdisciplinary cooperation leads to meaningful AI integration. The best uses of AI for heritage conservation emerge from collaborations between technologists, humanities scholars, and cultural experts. These collaborations also promote openness to the cultural landscape in which AI operates so that technological advances are culturally sensitive and ethically justified. The case studies in this study show how such partnerships have brought about creative approaches to preserve the dignity of heritage while tapping the power of AI to be revolutionary. The research also considers global inequalities in access to AI technology. Big institutions have already been leading AI-enabled heritage initiatives in tech-enabled parts of the world, but many marginalized communities lack the resources to do so. This digital divide threatens to create a warped account of world culture, favoring histories at the expense of others. The research calling for open-access AI tools, international funding networks, and knowledge-sharing networks that democratize AI-based heritage conservation must overcome this asymmetry. This paper provides a systematic plan for an interdisciplinary AI in cultural heritage and humanities research. It is a paradigm that emphasizes the marriage of technological progress and humanism, in which AI does not take over from human interpretation but augments it. By linking AI devices to cultural conservation standards, organizations can save heritage stories with sensitivity to their historical, social, and emotional resonance. In the long run, this research suggests that AI is unsurpassable and promising as a means of merging tradition and technology, providing new ways to access the past and prepare for the future. It offers theory and practice for more sustainable, inclusive, and innovative heritage management. It aims to encourage a more dynamic exchange between digital Creativity and human history so that cultural narratives are visible and alive forever. - Journal Article27 July 2023
An Investigation of the Advantages and Disadvantages of University Students as Avatars in Virtual Learning Spaces
Authors have noted the increasing importance of avatars in Higher Education, as more teaching is conducted virtually, drawing upon gaming conventions. However, it is also recognised that little is known about how students make use of avatars (especially over an extended period) and the subsequent impact on learning experiences. For the last three years, a university module has been conducted within a persistent virtual world – where students (49 in 2020; 95 in 2021; 122 in 2022) predominantly interact with each other and teaching staff in avatar form. Observation data constitutes 60 hours of video recordings of virtual world seminars. Students have also been surveyed (average 40% response rate) and interviewed. The experience of learning on this module while in avatar form has been extremely positive, with students expressing many advantages to being an avatar – including the ability to express oneself in original/engaging ways, the ability to move freely in the environment (less restricted by social norms), increased confidence to speak up in class, reduced concern over actual physical appearance, and being praised for their avatar. Nevertheless, disadvantages were also apparent, including the distracting nature of certain avatars, inappropriate behaviours, usability challenges in designing an avatar, and lack of sense of self. An initial design framework for the use of avatars in Higher Education is proposed.