This presentation describes an AI-assisted research study that investigated whether presenting students with ill-structured problems led to better or different patterns of student engagement in online discussion forums. It not only discusses the results of the research study but also explores the benefits and challenges of using generative AI for qualitative research.

Using ill-structured problems in the discussion prompts for the intervention group, researchers compared the frequency of discourse markers indicating the co-construction of knowledge in three online discussion forums. The researchers trained generative artificial intelligence in their classification system and compared the AI’s coding to that of two human coders.

This presentation briefly summarizes the scholarly literature supporting the instructional design approach and study methodology. It describes the coding system the researchers developed, the AI training technique, and the norming and calibration process. Finally, it presents preliminary study results and offers recommendations for online discussion prompt design.

Abstract: In post-digital educational contexts, where digital technologies are embedded, ubiquitous, and increasingly platformised, the central challenge for educational leaders is no longer adoption but quality control and governance. Many EdTech evaluation practices remain fragmented, focusing either on pedagogical affordances without strategic prioritisation or on operational considerations divorced from learning impact. This paper proposes a hybrid EdTech evaluation framework that integrates the SAMR model of pedagogical task transformation with the MoSCoW prioritisation method to support evidence-informed leadership decision-making across the technology lifecycle.

The framework is designed for use at three critical checkpoints – initial review, interim evaluation, and end-of-cycle assessment – enabling institutions to assess whether a technology is fit for purpose, delivering sustained pedagogical benefit, and aligned with organisational values and capacity. Informed by post-digital theory and critical political economic perspectives, including surveillance capitalism and platform enshittification, the framework foregrounds issues of power, data extraction, sustainability, and degradation over time. A case-based application demonstrates how pedagogical potential, strategic priorities, and ethical considerations can be made explicit and contestable within leadership processes.

The paper argues that combining pedagogical impact analysis with explicit prioritisation advances EdTech evaluation from a technical exercise to a reflective governance practice, equipping educational leaders to make transparent, defensible, and context-sensitive decisions in complex digital learning ecosystems.

The promise of artificial intelligence (AI) is high, yet AI integration in an online course benefits from understanding and balancing the benefits and challenges for students, educators, librarians, and instructional designers. This session reflects on how AI trends impact course design, course delivery, and learning assessment. It closes with thoughts about intellectual property, information integrity, and a vision of the future.

The accelerated adoption of Artificial Intelligence (AI) in recruitment has outpaced ethical governance with persistent gaps in oversight and explainability. This qualitative descriptive case study examined the influence of AI on recruitment processes, focusing on how organizational actors implement, govern, and experience AI in hiring. Data comprised semi-structured, web-based interviews with 10 participants, including HR leaders, diversity practitioners, and AI developers from the Southern United States, as well as document analysis. The document corpus, including 13 company policies, 7 governmental/regulatory sources, and 5 quasi-governmental sources, was used for context and triangulation. Analysis followed Braun and Clarke’s reflexive thematic analysis in NVivo and was interpreted through Socio-Technical Systems (STs) and the Ethics of Care (EoC) theories to prioritize human judgment and candidate dignity. Findings identified eight cross-cutting themes, including candidate experience and personalization, efficiency and innovation, human oversight and safeguards, fairness and inclusion, ethics and accountability, risks of misuse and bias, transparency and trust, and escalation protocols. Participants perceived the value of AI as inseparable from disciplined human stewardship, formalized governance routines, and auditable accountability mechanisms aimed to preserve candidate dignity. The study offers practice-ready recommendations for leaders and practitioners, including plain-language transparency standards, documented human review at AI-affected decision points, and accessible redress pathways to support responsible and equitable adoption. Transferability is bounded by the single-case design and context, indicating the need for multi-site and longitudinal inquiry to extend these insights.

As artificial intelligence and digital technologies increasingly shape educational systems, discussions often focus on innovation, efficiency, and technological adoption. However, less attention has been paid to how educational inequalities influence learners’ digital identities and their capacity to exercise agency in digitally and AI-mediated learning environments. This paper addresses this gap by examining how unequal access, skills development, opportunities for meaningful digital participation, and educational outcomes contribute to differentiated learner experiences in postdigital contexts.

The paper is based on a systematic literature synthesis drawing on education research, digital inequality studies, and critical technology scholarship. It integrates Biesta’s three purposes of education, Illeris’s theory of learning and identity transformation, van Dijk’s multi-level digital divide, socio-technical perspectives on digital identity, and critical digital literacy frameworks. Together, these perspectives support an understanding of artificial intelligence not as a neutral tool, but as a socio-technical mediator whose effects depend on pedagogical practices, institutional governance, and leadership decisions. A human–AI complementarity perspective is adopted to highlight how AI can either support or constrain learner agency under different educational conditions.

The analysis suggests that educational inequalities extend beyond material access to shape learners’ visibility, participation, and recognition in digital spaces, challenging persistent assumptions about “digital natives.” By foregrounding digital identity and agency as educational outcomes, the paper highlights the role of educational leadership in shaping human-centered and equity-oriented approaches to AI in education.

Educational responses to generative AI tend to be polarised: refusers who see it threatening academic rigour, values, and the very project of education itself, versus evangelists promising technological salvation. But what if we’re missing orientations between hand-wringing panic and techno-utopian euphoria?

This paper identifies three distinct orientations to pedagogical AI use. First, the instrumental/transactional: AI as vending machine (homework generator, text producer, academic integrity threat). Second, the meta-relational: drawing on Andreotti’s work, genuine subject-subject engagement emphasizing emergence, dialogue, and relational becoming. Third, semi-bounded pedagogical infrastructure: tools grounded in curated, discipline-specific content that enable both dialogue and structured knowledge work within a disciplinary frame.

I illustrate this third orientation through a proof-of-concept intervention: introducing NotebookLM to Business Economics students (ICT engineers) as they started preparing for final assessment. A small-scale exploratory study revealed distinctive patterns. While the tool offered both chat functionality and structured features, participants valued it for being “grounded in the slides” with “no hallucinations,” and primarily used the structured revision tools (quiz generation, flashcards, targeted Q&A) over open-ended conversation. Neither vending machine nor meta-relational critical friend, but something else.

This approach suggests design principles emphasizing epistemic grounding, disciplinary content, and scaffolded engagement. The patterns that emerged indicate a viable space worth further exploration.

The paper offers both a conceptual framework for thinking beyond the refusal-versus-adoption binary and a concrete example of semi-bounded AI design and use that educators can adapt to their own teaching contexts.

In postdigital schooling, platforms and AI reduce friction and make it increasingly rational for students to bypass demanding work. A salient case is reading long, linguistically and conceptually challenging literary classics—an educational practice whose benefits are slow, indirect, and difficult to measure. Large language models (LLMs) and summarisation tools intensify this tendency by turning texts into extractable information and rewarding fluent “hyperreal” performances of reading over sustained interpretive engagement. This conceptual paper argues that the key leadership problem is no longer only whether students comprehend what they read, but how schools can justify and sustain slow, effortful reading under LLM-rich conditions.

The paper integrates (1) experiential and interpretive theories of reading (Felski; Calvino), (2) research on deep reading in digital environments, (3) digital social reading as a motivational and interpretive resource, and (4) ethical and governance frameworks for GenAI in education (UNESCO, OECD, EU and national guidance). It develops principles for “AI-supported slow reading,” positioning AI as metacognitive scaffolding that sustains productive difficulty by supporting attention, questioning, monitoring, and interpretation without substituting for the act of reading.
The framework specifies boundaries between scaffold and substitute uses, task design criteria for productive difficulty, AI-supported routines for annotation and revision (e.g., post-reading Socratic prompts), process-based evidence structures, school-level transparency and shared rules, and equity-oriented differentiation safeguards. Targeting secondary education, the paper offers a principled basis for instructional design, curriculum work, assessment alignment, and leadership decisions that revalue slow engagement over fast outputs in postdigital literature education.

In postdigital societies, language learning unfolds across digital platforms, workplaces, and everyday social environments rather than within clearly bounded classrooms. For adult learners, this creates challenges that differ fundamentally from child language acquisition, which typically occurs through natural immersion in rich interactional ecosystems. Adult second-language learners often encounter language learning through formal instruction and digital applications that prioritise written language and standardised content, leaving gaps in spoken competence and contextual language use. Drawing on postdigital theory, adult learning research, and critiques of platform capitalism, this conceptual paper examines how educational leadership can better align digital tools, artificial intelligence (AI), and workplace practices to support meaningful and equitable language learning. Using Finnish language learning for international adults as an illustrative context, the paper offers a transferable framework relevant to educational leaders, HR professionals, and designers of online, hybrid, and workplace-based learning environments.

In multicultural learning environments, instructional designers face an important question: should they adopt universal frameworks that address general learner variability, or culturally grounded frameworks that address learners’ cultural contexts? This conceptual paper compares Universal Design for Learning (UDL) with a culturally grounded instructional-design model to examine points of alignment, divergence, and applicability across diverse educational settings. Both frameworks emphasize inclusive, flexible design through engagement, representation, and learner agency (CAST, 2018). However, they differ in epistemological orientation and in their use of culture. For instance, UDL addresses learner variability of affective, cognitive, and metacognitive dimensions, which can help to build a scalable foundation for learner accessibility. In contrast, a culturally grounded model, drawing on sociocultural theory and culturally responsive pedagogy, positions culture as central, situating variability within learners’ home cultures, institutional contexts, and emergent classroom cultures (Gay, 2018; Vygotsky, 1978). We propose a structured approach to aid instructional designers in selecting the most appropriate instructional design model under certain conditions. For example, we argue that UDL is most effective when accessibility and scalability are the most important priorities, whereas a culturally grounded model should be used when instruction must navigate multiple cultural systems, foster relational trust, and support co-construction of meaning. By aligning framework selection with the cultural complexity and instructional goals of the learning environment, designers can create learning experiences that address learner needs.

High school students in Hawai’i lack social and emotional awareness skills when transitioning into adulthood. These students tend to base a lot on what they learn in their home life, interactions at school, relationships with peers, social media, etc. Social and emotional learning (SEL) is an integral part of education and human development. SEL is the process through which all young people and adults acquire the knowledge, skills, and attitudes to develop healthy identities, manage emotions and achieve personal and collective goals, feel and show empathy for others, establish and maintain supportive relationships, and make responsible and caring decisions (CASEL, 2025). Island Pacific Academy wants to help its students by creating an advisory curriculum that can help teach students skills like communication between peers, so that they can then apply them in their everyday lives.

An online course was created about social and emotional awareness, specifically communication between peers. Each lesson focuses on a different overall topic: lesson 1 is communicating within my circle; lesson 2 is communicating outside of my circle; and lesson 3 ties it all together and is about communicating professionally. An online course introducing these three topics offers flexibility and accessibility. Online learning platforms also incorporate multimedia tools such as videos and interactive activities that can help them apply these communication skills in real-life situations. Evaluation was conducted with a pre- and post-survey given along with the module. Overall, the instructional module was easy to navigate, but the content was not always applicable to students. Some of the content was not appropriate for the age group, which created disengagement. The instructional module helped some students, whereas other students felt they already knew the information. Rise 360 proved to be effective in keeping engagement up for the students since we could include different embedded videos and interactive knowledge checks throughout the module. The use of Rise 360 was great for the students to have a different way of learning the content asynchronously, but there are drawbacks when it comes to tracking information and receiving results from any knowledge checks. Recommendations for future iterations would include working closer with the subject matter experts to have the most appropriate level of content for the students, as well as making sure that any interactivity was included for a reason. The amount of interactive blocks that can be included in Rise 360 can be overwhelming, so making a well-thought-out plan on how each lesson should be built would be more impactful for future students.