ATAIN - He Ara Mauri: Asking five large language models to calculate their value

 This session of ATAIN presented by Tanya Ruka (He Herenga Waka - Victoria University of Wellington).

She presents on:

He Ara Mauri: Asking Five Large Language Models to calculate their value

He Ara Mauri is a vitality framework grounded in Indigenous relational ontologies that asks a different question of AI: does this system sustain conditions for life, or degrade them? Applied to five frontier AI systems - ChatGPT, Claude, Gemini, Grok, and Meta AI - each system was asked to evaluate itself and then peer-review the others across planetary, human, and more-than-human domains. The result: every system failed.

Notes taken of presentation:

Began with a introduction to her background - starting from Native Land Digital - maintain one of the most widely accessed indigenous platforms in the world. Their remit includes providing educational resources to be used in classrooms, museums, universities etc. Develop indigenous platform. 

At the moment, funding has ceased but continues with work by passionate people.

Between 207 and 2024, she worked on the Te Aho Tapu Hou project - a Mātauranga Māori led research to create circular / regenerative cottage industries. At Victoria, there is one paper - training Korēro AI - understanding 'value' from an indigenous perspective. What happens when AI is tasked with looking into indigenous value systems.

Introduced the He Ara Mauri framework. Started investigating aspects of data sovereignty and what is indigenous AI. Coined AIRI (Artificial Indigenous relational intelligence) as a term to try to explain through a Native Land data, land and water acknowledgement chatbot.

LLMs build on linear, grab and dash and taking/scraping models. No data is safe. It is an extractive, hierarchical and predictive/control oriented. However, indigenous AI is relational in architecture, bussing from a GNN model (e.g. Deepseek) which are relational and layered. This allows use to be less, drawing more on human knowledge.

Kōrero AI created as a co-designed learning tools to move beyond static land acknowledgement. Instead of providing scripted responses, the chatbot invites users into reflection but asking 3 questions about their relationships to land and water. Across a year Feb 2025 to 2026, a million users have engaged. 

Summarised the process of creating the He Ara Mauri framework for system vitality in socio-technical systems. From this, sought to find out larger LLMs deal with the framework. 5 AI systems evaluated and each evaluated on environmental impacts, human impacts, more than human impacts, power, reciprocity and future consequences. Each system failed as they could not account for ecological consequence, more than human relations, intergenerational responsibility, power concentration and long-term vitality.

The systems could not describe themselves, acknowledge uncertainty, bias or harm, demonstrate responsibility, account of future flourishing and could not explain what they restore, instead of just that they take. Therefore, each system's responses reflected the business model, strategic priorities, and public positioning of the organisation that developed it.

Yet integration across education, public services, etc. is ongoing!!

Proposed some solutions - especially in the education context. Recommends, humans must remain responsible, teach AI critically, protect human knowledge creatin, embed future generations into governance. 

Therefore AI is not simply about efficiencies but we need to understand impacts beyond the now.

Education should be about a community of care and includes stewardship of knowledge, culture, democracy, environmental responsibility, future responsibility and technology should strengthen rather than replace relationships.

A thought provoking presentation bringing us back to the fundamental questions of what makes us human and how AI needs to be carefully handled, not just taken as a tool, but to carefully evaluate where it might fit, how it is used, who and when it is used, and how AI literacy must move beyond just using and evaluating but putting AI through different perspectives. 

Q & A provided good discussion on clarifying the principles presented along with discussion from others on implications and challenges. 

Developing curriculum for deep learning: the knowledge revival - book overvew

This is an open access book, 'Developing curriculum for deep learning' comes at a time when educators need to think about the purposes of education and their role as teachers.

The book is published by in 2025 Springer as part of their Springer Brief series in Education. The book has  5 chapters, written by eminent educator scholars - 

• Tim Surma ,
• Claudio Vanhees ,
• Michiel Wils ,
• Jasper Nijlunsing ,
• Nuno Crato ,
• John Hattie ,
• Daniel Muijs ,
• Elizabeth Rata ,
• Dylan Wiliam ,
• Paul A. Kirschner

All contribute to the chapters.

The short introductory chapter provides a rationale for the book along with a brief overview of the book's direction.

The next chapter summarises 'how knowledge matters' . It begins with a summary of how human's learn from a neuroscience perspective. The importance of attaining prior knowledge is introduced and discussed along with the importance of reading. The sociological and democratic perspectives of knowledge are also introduced.

Chapter 3 connects how curriculum is used to bring about the attainment of knowledge. The various ways curriculum contribute are introduced and discussed including the aspects of horizontal and vertical coherence and their relationships to disciplinary knowledge.

Chapter 4 provides concluding remarks and references.

The last chapter provides an executive summary. It reiterates the ways, perspectives on learning, and sociological and democratic aspects shift the ways knowledge is expressed in the curriculum. It argues for the need to ensure that there is always a knowledge rich curriculum in education, to assure the development of the individual and in turn, society at large.

The short book provides a good foundation, especially now in the age of AI, to ensure that as humans, we still treasure learning and knowledge. Worth a read for all educators. 

 

Handbook of critical studies of artificial intelligence - brief overview

This book, published by Edward Elgar provides an academic discussion on the various influences on AI including political, economic and social structures. A book preview provides the first chapter which has summaries of each of the chapters in the book.

The book is organised into 7 sections, each with many chapters. The sections are:

- AI and critical theory: conceptual discussions

- Imaginaries and discourses

- Political economy of AI: datafication and surveillance

- Transparency, ethics and regulation

- Bias, normativity and discrimination

- Politics and activism in AI

- AI and the automation of society

A book to dip in and out of. Chapters highlight the many implications of AI on society, work and human being. Hence, deploying AI in education is a double-edged sword. When (perhaps best beyond primary schooling), how (perhaps only when there is a good case that AI WILL support learning), what (with pre-requisite of strong underpinning critical thinking skills and perhaps some disciplinary foundations), and why (to support and enhance but not replace human thinking).

Unesco - Integrating AI into TVET

 Following on the heels from a recently released OECD report  on developing VET with AI, is the UNESCO work on integrating AI into TVET. This is a useful guide of institutions.

The guide hinges on: Human-centredness; equity, inclusion and non-discrimination, pedagogical purposedfulness; transparency, accountability and data protection; safety, sustainability and resilience; and critical engagement with AI. 

A relatively short report (just under 100 pages) but with good generalisable recommendations which are provided for policy makers, institutions, and teachers and trainers. There is a strong direction to ensure teacher professional development are provisioned, along with ensuring the voice of teachers is heard and taken into account. 

The annexes provide 'check-lists' for AI self-reflection and planning; AI risk register; and indicator framework to monitor and evaluate AI integration into TVET.

NZVETRF - master class #2 - Apprenticeships in Australia

 This morning, a webinar, hosted by the NZ Vocational Education and Training Research Forum (NZVETRF), facilitated by Josh Williams (Skills Group, NZ and Global Apprenticeship Network (GAN) with Gary Workman ( Executive Director, Apprentice Employer, Victoria, GAN Australia ) and Peter Canavan (Senior Policy Officer, Australian Industry Group).

Josh began with introducing himself, then the speakers. 

Josh presented following comparisons: NZ has 3.7% of employed workforce in training, 108,000 == in training, 2.9 million in workfo5ce. Australia, 2.1, 300000++ and 14.7 million in workforce. 27% of NZ school leavers into training and 40+% mature entrants, but in Australia, 51% do so, with 30% mature entrants. More females in NZ - 23% with 15% in Australia as new entrants. Both countries have decline of apprentices and trainees, with trend still going down. In NZ 25% decline between 2024 and 2025 :( Completions in NZ across 6 years 62% to 50%; for Australia 56% to 58%. 

Gary presented on the 'Return of Investment  (ROI) in supporting Australian Apprenticeships' (see report). When through definitions of ROI and also the reasons for the importance of ROI. Generally tries to measure and evaluate whether the time, financial investment, and resources dedicated to training result in tangible benefits. All of these are very variable across industries, businesses etc. both from government and wider community, employer and apprentices. Encourage audience to check through the report which provides link to online tool/calculator to try to work out the ROI. Shared examples provided through the calculator. In general, by the beginning of the 2nd year of the apprenticeship, employers start to even out and ROI rises for employers.

Summarised the key messages on ROI. Longer apprenticeships provide better ROI. 93% of apprentices who complete find full-time work in the same occupation. Group Training seen to provide good ROI. If apprentices change employers within the Group Training, there is a need to rotate the apprentices equitably so that employers are not always taking on novices and not obtaining the ROI. 

Peter then presented on the employer perspective on apprenticeships and traineeships. (see report). Australian Industry Group is an employer collective which also has a Centre of Education and Learning. Survey of employers finds 8 out of 10 employers see the importance of apprenticeships. 96% face barriers hiring apprentices. Around 1/2 indicate that they will employ apprenticeships if financial incentives were available - reduction of this will lead to less apprenticeships.

Therefore, employers weight up the economic equation, as per the ROI presented by Gary. Summarised barriers for hiring apprentices and the larges challenge was finding suitable candidates. Electrical and Plumbing have less difficulties but manufacturing has major difficulties. 1/3 indicate difficulty in training, and costs too high. Then shared how financial incentives are used for apprentices - mostly to offset costs/ reduce productivity associated with employing the apprentice. 

Warned that declining apprentice/trainee commencements are a lead indicator of future shortages. 

Discussion followed with questions around the employer/apprentice match. Why some apprenticeships struggle to attract apprentices? and trades are not promoted much at school as the general direction is still to prepare students for university. Better recruitment practices need to be supported with small businesses. Many do not have the resources to recruit and then have difficulties provisioning the training required. Chat also provided for interesting conversation within the Aotearoa context. 

Developing VET with AI - OECD report

The OECD has published their report on VET and AI - Developing VET with AI.

It is good to see a VET focused report on AI. This is especially given the role of VET in preparing people for the workforce, where occupations are incorporating AI into work tasks.

The main principles underpinning the report are: Human-centred use; ensuring diversity and inclusiveness; maintaining accountability; being transparent in understanding how, when, and for what AI is used and its limitations; and the importance of data quality, security and protection.

Policy considerations following on the principles are introduced, and discussed. These include: establishing strategy with clear purpose and support; managing risks through human-centred approaches; balancing diverse perspectives while ensuring equal access to AI; co-creating guidelines and building capacity for AI use and VET development; and strengthening data infrastructures and governance. 

The second chapter details the potential of AI in VET. In particular, to address the unique context of VET; the complexities of VET with multiple stakeholders; and to use AI to support VET development. A comparative study is undertaken across mainly EU countries with Australia data being also included.

The third chapter provides current and emerging use case studies. Policies and curriculum revision are compared across mainly EU countries. Chapter 4 then discusses the barriers and risk of leveraging AI for VET. The last chapter spells out policy considerations.

Overall, a macro/meso approach, especially comparing policy approaches across many countries. There is always much to learn from how countries handle the challenges of rapid changes as they impact on important social provisions, of which education is one of the main sectors. 

Assuring quality learning in a Gen AI integrated future

This is the third in a series on AI from the Tertiary Education Quality and Standard Agency (TESQA) in Australia. 

Assuring quality learning in a gen AI-integrated future: The role of adaptive capabilities follows on from two other reports - Assessment reform from the age of AI and Enacting assessment reform in a time of AI (summary on this blog).

In this report, adaptive capabilities in the age of AI are defined as drawn from the report authors' understandings of digital literacy tools to be used ethically and safely; distributed cognition as tasks shared between people, tools, artefacts and gen AI systems; hybrid cognition as thinking and learning within cognitive systems; and life long learning to sustain motivation, capability and adaptability. Agency and regulation from individuals, draw the four aspects of adaptive capabilities towards deeper disciplinary knowledge. 

The report argues well for the attaining of the above capabilities before moving on to how learning environments need to be reshaped to help learners. Propositions for policy and practice are presented:

- establish adaptive capabilities as  core graduate attributes.

- build institutional infrastructure for learning process evidence

- design learning environments that promote adaptive capabilities through evidence-informed practices

- transform pedagogical practice toward process-focused assessment

All in, a short but insightful and pragmatic report providing some ways forward into the age of AI.