Notes taken from the monthly ATAIN session.
This month, Joan Sutherland and Associate Professor Trish McCluskey Director Digital Learning from Deakin University on 'Human-led evaluation process: learning and teaching.
Joan presented, beginning with a acknowledgement of country and emphasising the human in technology.
Asked for what tools are used and why are these used.
What are the challenges? Drew on Laurillard (2012) Teaching is Design - knowledge acquisition, collaboration, inquiry, production, discussion and practice to place emphasis on the pedagogy and not on the tool itself. This was to address what tools, when, what good practice looks like and where to get support.
The initial evaluation surfaced the following: subjective view, lack of transparency, manual process, security risks, AI integration, workflow diversity, lack of alignment, duplication.
An explicit process required and aligned with institutional direction. Therefore, the current process was mapped; alignment and gaps identified; what does the research say? defined the ideal process; align and refine; how can automation be leveraged?
Shared process that was initiated. A new request is raised, need analysis and alignment, research and tool selection, pilot and test, evaluation and recommendations, implementation, ongoing monitoring and support, and evaluation for license renewal. The process is visible to all staff at the university. Each stage is recorded and available so that progress through the evaluation process is shared.
It is important to do the needs analysis is a human-led conversation to provide consistency, one source of truth, alignment, transparency and to avoid duplication. To identify gaps/duplication cover what challenges will the tool address? which activity types does the tool best align to (as per Laurillard and pedagogy approach) ? what key features expected of the tool? what specific tasks must this tool support? the tool is evaluated against a function and a feature matrix.
AI is challenging as it is probabilistic and will not provide the same output consistency. Privacy and cybersecurity are added challenges. Cross cutting principles include privacy-by-design, data minimization, security by design and ethics and compliance. Therefore, context needs to be taking into account along with user experience, AI components (transparency, clarity, relevance, usability, integration to ensure strategic alignment and pedagogical impact.
Shared an AI tools heatmap evaluation to help users/staff understand all of the above parameters.
Impact measured using SAMR (Puentedura, 2006), does it enhance and transform? However, it does not reflect the complexities of AI. Therefore, adding the layer of people, process, practice may help to provide a bit more representation for the wide range of AI.
A good overview of a process to evaluate technology tools that support teaching and learning. Q & A followed.
However, my take is that perhaps we are trying to fit a problem into an existing framework. AI is especially tricky as it is what the user does, that actually defines its pedagogical role. AI is therefore not a tool but more of a 'social-cultural' partner. It can be used as a 'sage on the stage' giving answers, a 'guide on the side' to support nascent conceptualisation and learning, or 'a muddler in the middle' a socratic opponent, thinking partner etc. AI literacy for our teachers and then in turn our students is a major key to reach the potential use of AI to support teaching and learning. Learning becomes a hybrid of human+AI rather than a 'tool'.
AI likely needs another evaluative process.