Degree apprenticeships - ConCOVE presentation

 This webinar presents the findings from a ConCOVE Tūhura project which undertook an evaluation of degree apprenticeship initiatives in Aotearoa.

The full report provides details. 

The presentation is facilitated by Eve Price with Brenden Mischewski, Keryn Davis from Architectural Designers NZ, and Tau Tua'i from Stevenson Tua'i Architectural Consultants. 

Eve introduced the work, to bring together workplace learning and degree qualifications. 

Brendon summarised the work undertaken over the last two years. He provided an overview of how degree apprenticeship is structured. Learners are employees first, completing a qualification. This helps real-world work readiness occur without incurring the costs of completing full time study. For many, who are working in the industry, attaining a degree becomes more difficult as they work longer. Taking 3 years out work or committing to many years of part-time study, is challenging when people have families and other commitments. Therefore, it is a job that leads to a degree, rather than a degree to get into a job.

Keryn confirmed there was a large number of members were interested in the approach. There was a need to expand the talent pool and to increase the diversity across the profession. Degree apprenticeship provides opportunities for many who have not traditionally been in the industry.

Tau provided a 'learner' perspective. He thought it drew on the inherent creativity in the Pacific community and the press of their communities to be in work and contribute to the family. 

Aotearoa context requires a rethink of how degree apprenticeships are structured and supported as small and medium businesses predominate across the economy. There are few large organisations with dedicated HR or People and culture divisions. Therefore it is important to extend the increase in VET and degree qualification attainment with ensuring there are opportunities for different ways to complete qualifications.

Keryn identified a key advantage to be 'at the get go' to support codesign of the qualification so that the apprenticeship would work for employers, employees and other stakeholders. 

As an employer, Tau reiterated that authentic learning is a key advantage. Most pick up and can be productive within a few weeks. Learning by doing means they are able to apply off-job learning to the work at hand. There is also a better match between learner aspirations and the company they complete their apprenticeship in. 

Brendon detailed how to best support SMEs and help them start small. Shared apprenticeship model is something that requires exploration to provide the wider roles that are common through the industry. Employers assistant to understand what they can bring in and be involved in co-designing the way an apprenticeships can be enacted. Degree apprenticeship help grow productivity and extend innovation in the industry.

Tau has supported school and tertiary learners in work experience and these have helped his company's approach to adopt degree apprenticeship. Both Keryn and Tau reiterated that employers often learn from their new entrants and also the learning that degree apprentices bring back from off-job learning. There is now a community of practice that can support new SMEs coming into the degree apprenticeship. Younger people bring the current  technologies back into the workplace.

Brendon detailed how good support for apprenticeship can look like. Introduced the various resources available to support employers and apprentices including study skills, pastoral care and the strategies to set aside study time. To earn, learn and strive, all must work together to make things work.

Keryn explained that in the pilot, relationships with providers are important. There is untapped potential to broker opportunities and work with the professional competencies to ensure that they work across different ways to complete a degree qualification. Collaborative relationships between employers and providers are a key. A 'pre-apprenticeship' programme may be useful to help learners try out the type of work required and expectations. Employers can then draw on the foundational skills and build on these through the apprenticeship. 

 Brendon summarised the various challenges and strategies that can be undertaken to work through them. However, if the motivation is there, these can be surmounted.

Q & A followed. 

Had to move to another meeting but degree apprenticeships should be a choice across many more professionals and disciplines. 

Transforming architectural design through AI/Generative design technologies - Dr. Mazharuddin Syed Ahmed

 Notes from a presentation to Ara kaimahi (staff) and ākonga (students) by Dr. Mazharuddin Syed Ahmeh, our Building Information Management (BIM) tutor.

Mazhar presents on the fundamentals of AI, LLMs and Generative design. He also covers AI governance (privacy, ethics, hallucinations, and misinformation); and emerging AI and LLM tools / technologies of relevance to architecture. https://www.theb!m.com/BIM-For-Beginners https://bim-in-nz.squarespace.com/bimtools

Began with overview of he journey to BIM via BS in Civil Engineering and through to PhD in education at University of Canterbury.

Proposed that technology disruption is increasing in speed and the need to keep up with the fundamentals underlying technology. A disruptive technology is one that displaces an established technology and shakes up industry or a ground-breaking product that creates a completely new industry. Used the mail service as an example - moving from pigeon post/pony express, to the postal system and then digitally into email and across social media.

Summarised the technology revolutions across the last few centuries - industrial/steam (1760-1820), electricity (to 1900), computing (1900S), digital (today), and artificial intelligence (2025-2030?). For computing, it has shifted from mainframes in the 1960s to mini, personal (1980s), desktop/internet (1990s), mobile (2000s) and wearable/everywhere/cloud (2014+). A key would be increased computing power along with progress in computer science. Gardner hype cycle for 2024 indicates the innovation triggers, peak of inflated expectaions, trough of disillusionment, slope of enlightenment and plateau of productivity (when citizen developers are able to utilise the technology). Humans adoption patterns can be summaries through the technology integration diffusion curve - innovators (techies), early adopters (visionaries) - the chasm - early majority (pragmatist), late majority and laggards (skeptics). 

Adoption of ChatGPT was the fastest - 1 million in 5 days, 100 million in 5 months, almost 7000 prompts a minute! - raising awareness of AI's potential into the mainstream.

Overviewed 'what is data' - presently much of data is unstructured and has had exponential growth, doubling every year. In comparison, traditional data, pre-digital, took 8000 years to double! Present human capability, makes it impossible for individuals to keep up with this volume of data being generated. The human brain has to take 'shortcuts' to help make decisions, leading to implicit or unconscious bias - of which are there many - see visualcapitalist for example!! One way to make sense of things is to use DIKW model - data, information (who, what, when, where), knowledge (how) and wisdom (why). Access to the internet (especially mobile access) is a precursor of individuals drawing on the knowledge of many - although there are implications if we move to 'onemind'.

Implications of AI on how technology is adopted and on jobs/ the world of work discussed. The need to attain data literacy is now paramount. In architecture, everything is data, every data follows a pattern, and every pattern can be modelled and predicted. Explained the concept of big data and data science principles. Defined and provided examples of LLMs (around since 2010) - large language models and their ability to predict 'the next word' based on word structure and sentence construction. Tokens serve as the fundamental units of text in LLMs. A token does not always represent a single word; it can also be made up of a group of characters. As a general rule, one toke is roughly 4 characters. 

ChatGPT/Copilot/Claude/Gemini prompts are more effective if they provide context, task, instruction, clarify, and refine. 

In human learning, we learn by observation, practice etc.to increase muscle memory and cognitive networks. Al-ML-Dl-Gen AL learnings through machine learning - recieve data, analyse, find patterns, make predictions, send answer. Provided examples of how ML is trained through supervised learning, with a 'reward model' used to refine the output. 'Transformers are used to interpret these outputs and convert to the type of response (text, pictures, multimodal etc.) required. AI moving into the near future able to undertake many of the functions of humans. Artificial General Intelligence (AGI) still only able to undertake some functions, so no worries!! - for the moment. AI is still prone to generating mis-information, is somewhat unreliable and may create 'hallucinations'. Pluses of AI need to be balanced with some of the disadvantages of ethics, dependency on data quality, risk of bias, complexity of development and maintenance, lack of emotional intelligence. AI governance is important.

Closed with the potential of AI in architecture. Numerical calculations (numbers, abacus, slide rule, calculator, mobile phone, VR), construction documentation (sketch, to plan, CAD (1980), 3D modelling (1990), BIM bringing in may layers of building data (2005) allow for this data to be drawn on for AI. Therefore, physical structures (cars, buildings, machines) can have a digital twin. Digital data can be used not only in BIM but in the internet of things (ioT) - smart buildings, connected constructions sites etc. From concept, through the design, analysis, scheduling etc, all can be digitised through dimensions of BIM. The AI-assisted design cycle - design details, validation design etc. is possible. Shared examples of the application of AI to architechutral work tasks.