Explore specialized AI resources focused on transforming teaching and learning. This page provides insights, guides, and practical applications that showcase how AI can be integrated into educational practices to enhance engagement, personalize learning, and improve teaching outcomes, offering educators and students cutting-edge support in the AI-powered classroom.
Mary Grush and Gardner Campbell
Michelle Strom
UNESCO
Sentient Syllabus Project (Creative Commons)
This guide is intended to introduce Artificial Intelligence (AI),
specifically Generative Artificial Intelligence (GenAI) and its
implications for teaching and learning. AI technologies are
developing rapidly, and this guide will be updated
occasionally to incorporate these developments.
This guide aims to answer the following:
● What is GenAI and how does it work?
● What are the current capacities and limitations?
● What are the current applications in teaching and learning?
The Rhodes University Policy on the Assessment of student
learning policy guides the assessment practices of the
university community. It emphasises the importance of
assessment to be valid, consistent and fair, which is
enhanced through the development of assessment criteria
associated with learning outcomes designed at the
curriculum development stage. This short guide is designed
to complement and be read in parallel to the Rhodes
University Assessment Policy.
This guide will introduce you to Artificial Intelligence (AI)
broadly and some current debates about the pros
(advantages) and cons (disadvantages) of using artificial
intelligence. We specifically focus on Generative Artificial
Intelligence (GenAI) tools like ChatGPT, Dall-E, Google Bard
and others.
Neil Kramm & Sioux McKenna | Published online: 11 Oct 2023
The dominant response within higher education to the emergence of free online text- and graphic-generating software has been a concern with identifying AI usage in students’ work. We argue that this is both a waste of time and neglects our educational responsibilities. A police-catch-punish approach to AI, as with the use of this process in relation to plagiarism, ignores the broader purposes of higher education. If higher education is understood as being a space for nurturing transformative relationships with knowledge, AI can be harnessed to enhance learning experiences. Such an approach would also enable a critical understanding of the limitations and ethical deliberations around AI usage. Those critical academics who emphasise transformative learning over surveillance-driven approaches are likely to foster more meaningful higher education experiences.
Lorna Gonzalez, Kristi O'Neil-Gonzalez, Megan Eberhardt-Alstot, Michael McGarry and Georgia Van Tyne | Published: Thursday, August 15, 2024
Drawing from three lenses of inclusion, this article considers how to leverage generative AI as part of a constellation of mission-centered inclusive practices in higher education.
The hype and hesitation about generative artificial intelligence (AI) diffusion have led some colleges and universities to take a wait-and-see approach.Footnote1 However, AI integration does not need to be an either/or proposition where its use is either embraced or restricted or its adoption aimed at replacing or outright rejecting existing institutional functions and practices. Educators, educational leaders, and others considering academic applications for emerging technologies should consider ways in which generative AI can complement or augment mission-focused practices, such as those aimed at accessibility, diversity, equity, and inclusion. Drawing from three lenses of inclusion—accessibility, identity, and epistemology—this article offers practical suggestions and considerations that educators can deploy now. It also presents an imperative for higher education leaders to partner toward an infrastructure that enables inclusive practices in light of AI diffusion.
Dr Philippa Hardman | Oct 03, 2024
Dr Hardman explore how AI is reshaping our understanding of what learning and the assessment of learning looks like in both higher education and corporate L&D.
One thing that's become crystal clear is that established models of learning - like Bloom's Taxonomy - are struggling to keep up with the impact of AI, both in academia and in the workplace.
Consider this: in a world where AI can retrieve and present information in seconds, is memorisation still a critical skill? Or should we be focusing on higher-order thinking skills that AI can't replicate - skills that education has aspires to deliver for decades, and which the the World Economic Forum has identified as crucial for the future workforce and global economy?
But here's the exciting part: this challenge is also an opportunity to reimagine how we define and measure “learning” in new and more effective ways.
In this newsletter, Dr Hardman explores some of the problems that AI has presented to establishes systems of learning and assessment and put forward a vision for a new Post-AI Learning Taxonomy that might help us to shift from a state of panic and AI detection to one of experimentation and innovation powered by AI.
Mike Perkins, Leon Furze, Jasper Roe, Jason MacVaugh | DOI: https://doi.org/10.53761/q3azde36
Recent developments in Generative Artificial Intelligence (GenAI) have created a paradigm shift in multiple areas of society, and the use of these technologies is likely to become a defining feature of education in coming decades. GenAI offers transformative pedagogical opportunities, while simultaneously posing ethical and academic challenges. Against this backdrop, we outline a practical, simple, and sufficiently comprehensive tool to allow for the integration of GenAI tools into educational assessment: the AI Assessment Scale (AIAS). The AIAS empowers educators to select the appropriate level of GenAI usage in assessments based on the learning outcomes they seek to address. The AIAS offers greater clarity and transparency for students and educators, provides a fair and equitable policy tool for institutions to work with, and offers a nuanced approach which embraces the opportunities of GenAI while recognising that there are instances where such tools may not be pedagogically appropriate or necessary. By adopting a practical, flexible approach that can be implemented quickly, the AIAS can form a much-needed starting point to address the current uncertainty and anxiety regarding GenAI in education. As a secondary objective, we engage with the current literature and advocate for a refocused discourse on GenAI tools in education, one which foregrounds how technologies can help support and enhance teaching and learning, which contrasts with the current focus on GenAI as a facilitator of academic misconduct.
Offline Website Software