Dr Jodie Torrington spent more than two decades as a primary school teacher. She worked in four Australian states and territories, and in New Zealand. She was a senior teacher for the last twelve of those years, leading teams of colleagues and running the staff sessions that put learning research into their practice.
She brought that classroom experience into research. Her doctorate at Macquarie University examined how young children manage their own learning when they work with digital tools, which researchers call self-regulated learning. She also holds two master’s degrees, one in research and one in advanced practices in teaching and learning.
She now manages the Teachers’ Learning Hub at Macquarie University. She turns research into professional learning that teachers can use, and she leads the team that builds it. She also teaches the people who are training to become teachers. She is a researcher on a 2026 project, funded by a James N Kirby grant, that runs workshops teaching high school students to work with AI.
Her research asks who students believe controls what AI gives them. She built this research program with Brian Ballsun-Stanton, and they named the question the AI-Locus of Control. Students at its external pole feel they must accept whatever the tool produces, or refuse to use it at all. Students at its internal pole steer the output and judge it, sentence by sentence.
The continuum grew from a study of what happened when students were taught, explicitly, how to work with AI. She and Brian Ballsun-Stanton then built a 26-hour Coursera course on generative AI for educators. Coursera invited her to share the evidence behind her approach. Its AI Dialogue team asked her for pedagogical advice in 2026.
In 2025 she and Brian Ballsun-Stanton, with colleagues in the Faculty of Arts, were Highly Commended in the Vice-Chancellor’s Award for Excellence in Education. The Faculty of Arts also gave her a Highly Commended that year in its Professional Staff Awards for Excellence in Innovation.
The Teachers’ Guild of NSW made her an Honorary Fellow in 2022. That award recognises educators who have made major contributions in their own sphere of work and across the profession. She is currently part of an international advisory team on AI learning design, which is supported by UNESCO. Her purpose has not changed since her first classroom, which is to help teachers.
The AI Prompting Framework is a structured, six-stage guide (numbered 0 to 5) for working with generative AI in a way that keeps the user's knowledge, expertise, and judgment at the centre of the process. Rather than treating AI as a source of ready-made answers, it positions the tool as a collaborator whose output the user directs, scrutinises, and ultimately owns.
The framework moves from an initial decision about whether AI is appropriate for the task at hand (Step 0), through the preparation of a clear, well-contextualised first input with a built-in self-check (Step 1), to reviewing that first response (Step 2), interacting iteratively while retaining control of the exchange (Step 3), reflecting on whether the final output genuinely reflects the user's own knowledge (Step 4), and finally documenting effective prompts and being transparent about AI use (Step 5). Each stage is paired with a reflective question, such as "Have I provided enough context?" and "Am I happy to put my name to this?", that prompts users to pause and exercise judgment throughout.
Designed to be general-purpose, the framework applies across disciplines and professional contexts wherever people want to use generative AI deliberately, critically, and accountably.
Our proposed AI-Locus of Control framework has emerged from our recent longitudinal study on the impact of explicit teaching about AI literacy on students' agency and control beliefs in human-AI interactions (https://osf.io/preprints/edarxiv/6mke5_v3). From our analysis, we identified four key dimensions or tensions in AI-Locus of Control:
AGENCY: Does the AI have agency, or does the human retain it? Students with external LOC felt they had to either accept AI output or reject the entire idea of using AI. Students with internal LOC realised they could steer, adjust, and control output on a sentence-by-sentence basis.
EXPERTISE: Is AI better than human, or an input to human judgement? Some students initially treated AI as an expert that couldn't be questioned. Through the unit, they learned that their own prompting and setup was crucial to producing output they felt comfortable claiming as their own.
EMBODIMENT: Is there a mind behind the mirror, or is it a tool to be used? Early in the semester, students engaged with AI as if there was an intelligence behind it. By the end, they understood they held sole responsibility for populating the context window with facts and scaffolding.
PRAXIS: Automation versus augmentation? Some students used AI to automate their assignments entirely. Others learned to dip in and out, using it for partial ideation, sense-checking, and editing - augmenting rather than automating their process.
These four tensions give us a richer understanding of what AI-LOC means in practice.
The study measures whether students' beliefs and orientation about AI shifts across a semester of teaching. This orientation, a student's AI-Locus of Control, is whether they direct the AI tool or defer to it. The design is longitudinal, mixed-methods, and Bayesian. It runs across three cohorts at one university. Both surveys are collected and sealed, and no response has been examined. This deposit lets a reviewer inspect the parts of the analysis plan fixed before the data are seen: the survey instrument, the qualitative codebook, the design simulations that set the evidence thresholds, and the record of how AI assisted the work. The survey responses are not here. They are sealed now and embargoed to Stage 2.
The Typology of Generative AI Tools for Education provides educators with a list of generative AI tools arranged into nine categories that are currently being used in educational contexts. This typology follows the previously published Typology of Free Web-based Learning Technologies (Bower & Torrington, 2020) and the Typology of Web 2.0 Learning Technologies (Bower, 2015), representing the evolution of educational technology into the generative AI era. To create this typology, 211 educators from nine countries spanning early childhood through higher education completed a survey in late 2025 about their generative AI tool usage. Tools that were reported by two or more educators were included in the typology. A total of 50 unique AI Educational tools were included in the Typology, and have been arranged into nine overarching categories: General-Purpose Large Language Models, Image Creation Tools, Audio and Music Generation Tools, Video Generation Tools, Presentation Generation Tools, Research and Study Tools, AI Tutoring and Chatbots, Custom Education Tools, and Other Miscellaneous Tools. Brief descriptions and links are provided for each tool to support educators in making informed decisions about which tools might suit their teaching and learning contexts. The generative AI landscape is rapidly evolving, and it is noted that some tools offer functionality across multiple categories. This typology represents a snapshot of educator-reported usage patterns in 2025-2026, and offers educators a touchstone for the appropriate selection of Generative AI technologies in their teaching.
TWG 5 focused on developing and implementing effective strategies for enhancing AI literacy and agency of teachers, equipping them with the knowledge and skills necessary to integrate AI into their teaching practices. Explorations covered curriculum design, professional development programs, practical classroom applications, and policy guidelines aiming to empower educators to confidently utilize AI tools and foster a deeper understanding of AI concepts among students.
Poster to support successful prompting by teachers and researchers.
Good AI prompts are essential to predictably and consistently getting a desired output.There are no single phrases that work across all situations - AI will simply amplify the user’s capabilities, creativity, judgement and discernment.
These effective prompting strategies will help develop your own skills and strategies with AI prompting.
Text:
Start with a goal: what is the single thing you are trying to achieve?
Add context and details: requirements, instructions and parameters. AI needs clear scaffolding so it’s set up for success. Don’t ask it to search for facts - it’s not a search engine.
State your role, so it knows what register to use. The use of words influences its sample and response. Tell it what you want and who the output will be for.
Work as thinking partners: Tell the AI to “Ask me one critical question at a time until we achieve our goal”
Check for clarity: Include a final sentence for the AI to scaffold the task to make sure what you said is what you meant. “To begin, please functionally decompose the task.”
Use examples. AI is effective at imitation, and if you give it a few examples of what you want, it can copy their form.
Longer prompts, rather than shorter: Make sure that you provide all the details it needs. Edit your prompts if errors occur, or start over.
Clear expectations: At the end of each prompt, end with a single, specific task.
Have a real conversation: Go back and forth a few times before you reach your answer. Asking it to ask you for clarification highlights blank spots in your thinking.(Also, you can prompt it to help you design prompts…)
Avoid “AI-slop” - Would you be comfortable putting your name to this? Have you interacted using some effort? Make sure you know enough to be able to validate the output.
Background: As AI disrupts education systems worldwide, there is an urgent need for students to develop AI literacy while maintaining human agency. While extensive research has examined self-efficacy in technology contexts, there is a notable gap in understanding how locus of control (LOC) specifically applies to human-AI interactions in educational settings. LOC is defined as the belief that outcomes are contingent on one's own behaviour versus external forces. Objectives: This study examined how explicit instruction and guided practice with multiple frontier Large Language Models (LLMs) impact students' agency and control beliefs in human-AI interactions. Methods: A longitudinal qualitative case study followed 12 undergraduate students through a semester-long experimental AI unit. Data collected through surveys, focus group discussions, and weekly observations were analysed thematically, using LOC theory as an interpretive framework. Results and Conclusions: Students demonstrated a clear progression toward internal AI-LOC, developing critical awareness and agency in their AI interactions. The findings revealed that sustained, scaffolded AI instruction enabled students to view themselves, rather than the technology, as the primary driver of outcomes. This study contributes to AI literacy theory by demonstrating that locus of control, rather than self-efficacy, may be the critical construct for understanding human agency in AI interactions, introducing the concept of AI-LOC as a new theoretical framework for education research.
This dataset contains educational materials from an AI-focused course, including: - Student assessments from AI History, AI Philosophy, and AI Politics streams - Class transcripts from various sessions - Course materials, schedules, and rubrics
Effort regulation, or the ability to manage and control one’s effort during learning, is a consistent predictor of academic achievement in higher education. The rapid rise of artificial intelligence and its impact on higher education learning environments raises questions about how students are adjusting how they apply and manage effort in their learning. Examining the dynamic interplay between use of AI and effort regulation is necessary to develop effective higher education practices and policies that support improved learning processes for students in the context of AI. This study employed a mixed methods design to explore 1,316 undergraduate pre-service teachers’ perceptions of the relationships between effort regulation and AI use. Quantitative findings showed that students with stronger effort regulation were significantly less likely to use AI. Similarly, qualitative findings showed over half of the sample (52.59%) perceived that using AI could lead to overall disengagement from university work, learning loss, and lack of skill development. When triangulated, findings suggested that students who perceived effort regulation as critical to their learning and skill development were less inclined to use AI because they perceived AI use as undermining their effort regulation and meaningful engagement. These findings have implications for higher education practice: these suggest a need to explicitly communicate to students the importance of effort regulation in learning and the appropriate ways in which AI can be used to enhance effort regulation.
Generative artificial intelligence (AI) has disrupted education systems worldwide. This disruption necessitates pedagogical approaches that embrace uncertainty while developing student agency. We examined how decoupling task success from assessment outcomes created environments where students developed critical AI literacy through structured risk-taking. Drawing on Transformative Learning Theory and Rumsfeld's epistemological matrix as interpretive frameworks, we analysed an experimental undergraduate AI unit across three disciplinary streams: Ancient History, Philosophy, and Politics and International Relations (). Data included student reflections, classroom observations, and AI interaction logs collected over a 13-week semester. Our pedagogical framework operationalised four interconnected pillars: risk-embracing assessment structures, intentional classroom culture development, systematic navigation of technological uncertainty, and facilitation of transformative learning experiences. This paper presents the implications of these pillars for 1) educational theory, where productive failure serves as an effective pedagogical strategy; 2) educator praxis, viewing AI as a textual technology that extends the capabilities of the humanities; and 3) implications for the university teaching context, where AI-enabled teaching should focus on reflection and process rather than demonstratable competencies.
There has been widespread media commentary about the potential impact of generative Artificial Intelligence (AI) such as ChatGPT on the Education field, but little examination at scale of how educators believe teaching and assessment should change as a result of generative AI. This mixed methods study examines the views of educators ( n = 318) from a diverse range of teaching levels, experience levels, discipline areas, and regions about the impact of AI on teaching and assessment, the ways that they believe teaching and assessment should change, and the key motivations for changing their practices. The majority of teachers felt that generative AI would have a major or profound impact on teaching and assessment, though a sizeable minority felt it would have a little or no impact. Teaching level, experience, discipline area, region, and gender all significantly influenced perceived impact of generative AI on teaching and assessment. Higher levels of awareness of generative AI predicted higher perceived impact, pointing to the possibility of an ‘ignorance effect’. Thematic analysis revealed the specific curriculum, pedagogy, and assessment changes that teachers feel are needed as a result of generative AI, which centre around learning with AI, higher-order thinking, ethical values, a focus on learning processes and face-to-face relational learning. Teachers were most motivated to change their teaching and assessment practices to increase the performance expectancy of their students and themselves. We conclude by discussing the implications of these findings in a world with increasingly prevalent AI.
It is well‐established that being a self‐regulated learner is beneficial academically, motivationally and is considered essential for productive life‐long learning. Despite this, there is limited evidence examining how different measures of self‐regulation for learning (SRL) relate to task performance for young students learning in digital contexts. This study investigated the relationships between different measures of SRL of elementary school students ( N = 48, M age = 10.75) while using a computer‐based learning environment, and their association with task performance and teacher rating of student SRL ability. Results confirmed the most effective measure of SRL, in terms of its relationship with, and predictability of task performance, was a self‐report written response, whereby students identified and explained known SRL strategies, such as how to plan, monitor or complete their work. Teacher ratings of their students' metacognitive capability were significantly correlated with task performance and with two self‐report instruments: the Junior Metacognitive Awareness Inventory and the SRL written response. Associations between actual observed self‐regulation behaviours in a computer‐based learning environment, using Azevedo et al.'s coding framework and how students self‐reported their knowledge and understanding about SRL, were weak. Observations of young students' SRL behaviours in computer‐based learning environments were not significantly related to task performance. Better understanding of these relationships will help educators and researchers to know where they should focus their attention in terms of developing elementary school students' self‐regulatory capabilities in digital contexts, as well as the reliability of self‐report measures of SRL as relative to observations of self‐regulation and task performance. Implications for teacher practice are also discussed.
Practitioner notes
What is already known about this topic
Students need to use self‐regulation for learning (SRL) strategies in digital contexts.
Self‐regulation strategies need to be explicitly taught to students as they are not guaranteed to become automatically acquired.
Being a self‐regulated learner leads to improved academic performance, engagement and motivation.
What this paper adds
Empirical evidence addressing the associations and patterns between various measures of SRL for young students learning in digital environments.
Student self‐report explanations of known SRL strategies was the only significant predictor of student task performance.
Coding of young students' SRL behaviours in digital environments do not relate well to task performance.
Implications for practice and/or policy
Understanding the associations between young students' self‐report of SRL and how this relates to their actual SRL behaviour while using digital technology is critical to supporting student learning and success.
Learning to better articulate self‐regulation strategies may result in greater consciousness and application of self‐regulation strategies in digital contexts, which in turn could improve task performance.
Eliciting explanations from students about SRL strategies may be more informative and expedient than conducting and analysing individual observations in digital contexts, to determine the general self‐regulatory knowledge and understanding of young students.
Little is known about the strategies elementary school students use to self-regulate their learning while in a hypermedia environment. This exploratory study investigated the self-regulatory strategies that young students ( N = 48, M
age
= 10.75) utilized while individually completing a 20-min online research task about space. Video data was coded using Azevedo et al.'s (2004) established coding scheme for analyzing self-regulatory behavior in hypermedia environments. Results showed that young students spent the majority of their time using cognitive strategies ( M = 75.26%) to read and summarise information to complete the task. Little time was taken to plan ( M = 6.99%) or monitor ( M = 5.92%) their work or learning processes, which are key attributes of effective self-regulation. The study reveals the disparity between the ability to navigate within a hypermedia environment and utilizing planning and monitoring processes to enhance learning while using digital tools. This study highlights the need for the explicit teaching of planning and monitoring strategies in order for young students to develop the full range of self-regulation skills they need when using technology, for instance while learning from home during COVID-19. Implications for curriculum policy and teacher practice are discussed.
Despite the proliferation of multimedia devices in elementary classrooms, there is limited research examining teacher‐created video instruction, particularly regarding its effect on academic growth and engagement. This study investigated the effect of teacher‐created computer‐based video instruction (CBVI) using iPads on students' academic, behavioural and affective learning in elementary classrooms. The study used a repeated‐measures design with counterbalancing to measure the effects of CBVI during mathematics lessons on student achievement scores, time on‐task and attitudes towards learning. Three year three classes ( n = 49) completed three lessons, each using a different mode of instruction: CBVI created by the class teacher, CBVI created by a stranger, and a traditional live lesson delivered by the class teacher. Results were analysed using a Linear Mixed Model. No significant growth in performance was detected during video instruction, however a significant growth result was achieved for the traditional live teaching mode ( p < 0.001), possibly attributable to the longer duration of experimental session. Behavioural engagement was considerably higher during CBVI lessons than live lessons and students preferred their teacher's voice during CBVI. Three teachers were interviewed to examine how CBVI affected teaching and learning, with two main themes emerging: (1) positive impacts of CBVI upon students; and (2) positive impacts on teacher wellbeing. This research indicates benefits for students and teachers when using teacher‐created CBVI. Further research is needed to better understand the factors that influence cognitive development of students using CBVI and to also further explore the effect of CBVI on teacher wellbeing.
, Lay Description
What is already known about this topic
Video as an instructional method has been used to deliver content to learners for over 40 years.
Current mobile technologies have the inbuilt capability to film, edit, upload and access custom‐made instructional videos.
Learning through video instruction is enjoying renewed popularity due to Internet capability, increased connectivity and device affordances.
Video can promote self‐paced, controlled, personalized learning experiences for students.
What this paper adds
Teacher‐created video instruction has been an overlooked pedagogical approach in mainstream elementary classrooms.
Students are highly engaged behaviourally when completing teacher‐created video lessons.
Students prefer to hear their teacher's voice on the video, rather than a stranger's.
Using teacher‐created video instruction in the classroom positively impacts on teacher wellbeing.
Implications for practice and/or policy
Using teacher‐created video instruction in the classroom provides a way to deliver differentiated content to groups or individuals.
Students have the autonomy to self‐pace and control their learning within the close proximity of the teacher.
Teachers are able to provide individual support and attention to students during the video instruction without affecting general learning.
High student engagement can positively impact the classroom dynamics and environment.
