New Practice Notes for Teaching Practitioners
I’m delighted to announce the launch today of a series of weekly Practice Notes for learning and teaching faculty, lecturers, and teachers in tertiary or higher education contexts. These PDFs are free to download and share with your colleagues. The first issue is giving feedback that students will actually use. The next issue will deal…
Is your institution stuck diagnosing the AI problem without a clear roadmap for action?
The article emphasizes the urgent need for academic leaders to address curriculum inertia in the age of AI. It presents a 12-month action plan for Deputy and Pro Vice-Chancellors that includes naming outdated curricula as a key challenge, conducting thorough audits, and implementing rapid reform pathways.
Is your university’s governance architecture actively blocking your AI curriculum strategy?
The piece discusses how traditional academic governance is failing to adapt to the rapid impact of AI on curricula. It identifies three primary failures: slow review cycles, unclear ownership of AI initiatives, and a focus on compliance rather than transformation. Senior leadership must lead changes to effectively respond to these challenges.
Are we mistaking activity for strategy when it comes to AI in higher education?
Senior leadership discussions on AI often overlook the critical issue of curriculum inertia in higher education. As the labor market demands AI fluency, many graduates feel unprepared. Institutions risk falling behind in graduate employability, student retention, and reputation unless they adapt their degree programs swiftly to meet these changes.
Most universities are still designing courses the way they always have …
Many universities continue to design courses focused on lecturers rather than learners. The latest Substack discusses shifting to a collaborative and transparent model, addressing topics like academic autonomy, neuro-inclusive design, the importance of institutional memory, and the challenges posed by the AI divide. This ongoing series supports an 8-Stage Learning Design Framework.
From Panic to Practice: What Does an AI-Ready Curriculum Actually Look Like?
Following up on last week’s post about higher education focusing on the wrong AI emergency, my latest Substack shifts from the problem to the solution. If we want graduates to thrive in an AI-integrated world, a generic “AI literacy” module won’t cut it. Instead, we need to develop professionals who can think and work alongside…
From Content Delivery to Learning Architecture: The New HE Paradigm.
Higher education is shifting from a traditional transmission model to intentional learning design, emphasizing active knowledge construction and mastery. Research supports the effectiveness of active learning, while Learning Design emerges as a critical discipline. The pandemic and Generative AI have accelerated this transformation, redefining educators as Learning Architects focused on curated, purposeful learning experiences.
Is higher education focusing on the wrong AI emergency?
Many universities are currently focused on updating academic integrity policies and banning AI, viewing it as a cheating issue. However, this overlooks the need to redesign learning experiences for an AI-integrated world. The real crisis lies in faculty development and curriculum design, which are insufficiently addressed for modern labor market demands.
Introduction to the 8-Stage Learning Development Framework
Many academics, while being subject-matter experts, lack training in course design, leading to ineffective curricula that reflect their own experiences rather than student needs. The 8-Stage Learning Design Framework (8-SLDF) addresses this issue, emphasizing Constructive Alignment and five development areas while encouraging honest engagement with AI in the design process.
Why do we design for reuse and renewal?
The article discusses evolving curriculum development trends, advocating for a shift from static models to dynamic, modular approaches. It highlights the importance of granular mapping, preventing loss of educational resources through standardized tagging, and fostering collaborative content curation. This framework supports institutional agility and continuous course renewal.
Why do we design to enable academic and learner analytics?
The post emphasizes the importance of using student data to enhance course design rather than merely reporting past outcomes. It highlights the roles of Educational Data Mining, Academic Analytics, and Learning Analytics in understanding learner needs. Key strategies include building responsive pathways, designing supportive touchpoints, and anticipating emerging technologies to optimize learning experiences.
Why do we design with a view to the evolving nature of work?
The 8th principle of learning design emphasizes the need for educators to adopt a proactive, future-oriented approach amid rapid technological changes. As skills quickly become outdated, the article advocates for developing Futures Literacy and focusing on transversal meta-skills. By leveraging real-time data, curriculum design can better prepare students for an evolving workforce.
Something went wrong. Please refresh the page and/or try again.