The three essential dimensions of education systems and curricula in terms of how students interact with that curriculum:

1. Engagement: Students might engage because they recognize the value of what they’re learning, even if they don’t particularly enjoy it. Multiple factors drive this engagement—teaching styles, content relevance, cultural factors—all contributing to whether students see value in what they’re learning.
2. Commitment: This describes how willing students are to invest effort and persist with the learning process, whether through following established structures or driving their own learning forward.
3. Agency: This means having the ability to make decisions appropriately at their age level, having choices that genuinely impact outcomes, and opportunities to make good choices. 

From some initial research conducted over the last month, I’ve found that the vast majority of Instructional Designers choose their AI tools based on UX preference and cost.

Meanwhile, in the workplace, most L&D teams are allocated AI tools based on the existing tech stack rather than tool performance. TLDR: If your org already uses Microsoft Office Suite and Teams, you will more than likely be allocated MS Copilot as your AI sidekick for the L&D workflow.

Artificial Intelligence promises to transform education by tailoring instruction, automating routine tasks, and reshaping teachers’ roles. UNESCO stresses that AI “has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and accelerate progress towards [Education 2030]”. Governments and experts foresee AI becoming pervasive in classrooms, but emphasize a human-centric approach. For example, UNESCO and OECD frameworks urge that AI should “support teachers in their educational and pedagogical responsibilities,” with human interaction remaining at the core of education. In practice, schools will use AI tools for everything from designing lessons to grading assignments, while policymakers plan to steer AI adoption carefully to ensure inclusion and equity.

In the last 2-3 years, AI avatars have evolved from futuristic novelty to an increasingly mainstream feature of corporate training. With platforms like Synthesia making avatar-based video creation accessible to every L&D team, we're already seeing a dramatic shift in how organisations deliver training at scale with AI avatars (literally) front and centre. This shift represents more than just a technological upgrade—it's fundamentally changing how we think about the relationship between instructor and learner.

The business case is compelling: avatar-based training can save hours of production time and millions of dollars each year by eliminating the need for human presenters, reducing reshoot costs, and enabling rapid content updates. Organisations can create consistent training experiences across global teams, overcome scheduling constraints with subject matter experts, and scale personalised instruction in ways that were previously impossible.

In Canada, some professors are making a similar move amid widespread reports of AI cheating, while others are testing out oral exams or finding ways to incorporate AI. Six in 10 Canadian students said they use generative AI for their schoolwork, according to an October 2024 study from KPMG in Canada.

"We are definitely in a moment of transition with a lot of our assessments," said Karsten Mundel, co-chair of the University of Alberta's AI steering committee.

The enthusiastic response highlights a growing recognition across industries: In an era of rapid technological change and artificial intelligence (AI) development, uniquely human abilities are more crucial than ever for workplace success. According to a recent Deloitte Insights report, 92% of companies now say that human capabilities and soft skills matter more than hard skills.

Clark uses a different term for these talents – power skills – to emphasize their critical importance. He and Sandra Sjoberg, associate dean and director of marketing and innovation programs at WGU School of Business, have become strong advocates for their central role in higher education today.

Metacognition has the common definition of “thinking about thinking” (see The Best Posts On Metacognition). In other words, it is the self-awareness to know what our strengths and weaknesses are, and how and when to apply the former and compensate for the latter.  Broadly explained, learners applying metacognitive strategies plan in advance for effective learning, monitor and make adjustments during the lesson/activity to maximize their learning, and reflect afterwards about which learning strategies worked and which did not for them.

La integración de la educación disruptiva y la IA-AGI en la educación superior, dentro del contexto de la realidad aumentada (RA) y la realidad extensiva (RX), abre un abanico de posibilidades que transforman cómo los estudiantes interactúan con el conocimiento. A través de un escenario multimodal, podemos explorar cómo estas tecnologías permiten personalizar, ampliar y hacer más inmersivo el proceso de aprendizaje.

Al integrar modelos de IA para personalizar el aprendizaje, hemos explorado cómo la memoria contextual y los agentes inteligentes pueden contribuir a un entorno educativo donde cada estudiante pueda avanzar a su propio ritmo, tomando decisiones informadas sobre su proceso de aprendizaje. Como afirman Goodfellow et al. (2016) en su obra Deep Learning, las redes neuronales profundas y los algoritmos avanzados pueden replicar la memoria cognitiva de los estudiantes, permitiendo a los sistemas aprender y adaptarse con base en sus interacciones previas, generando una experiencia educativa dinámica y enriquecedora. Este tipo de aprendizaje autónomo y personalizado se alinea con las aspiraciones de Farnós, quien sostiene que la IA puede servir para potenciar y revolucionar la educación superior mediante la creación de entornos inteligentes que se adapten a las necesidades de cada estudiante.

In a recent Boston Globe op-ed, two researchers proposed a linguistic fix to an ontological dilemma: rename our relationships with AI. Rather than referring to generative systems as “coworkers” or “collaborators,” they suggest we replace the human “co-” with a machine-coded “c0-”: c0worker, c0creator, c0mpanion. The goal is to reassert the boundary between human and machine by embedding it in our language—clarity by typography.

It’s a clever gesture, but telling. Faced with the entangled realities of cognitive labor shared across human and machine systems, the instinct is not to inquire but to quarantine. The prefix becomes a firewall. We’re no longer debating what AI is or does; we’re drawing thicker lines around who gets to count as meaning-making. This isn’t just terminological hygiene—it’s epistemological panic.

The persistence of the binary—human as subject, machine as tool—isn’t just about fear. It reflects an older philosophical architecture in which cognition is tied to consciousness, and meaning to interiority. When AI generates output that seems intelligent, we either mistake it for a person or strip it of all significance. The problem isn’t that machines confuse us. It’s that our frameworks for interpretation haven’t kept up.

The AI in Education Leadership Catalyst community of practice was intentionally designed to foster the strategic leadership and collaborative networks necessary to navigate the complex implications of generative AI. As co-designers, we envisioned a space where leaders could contemplate big questions: How do we lead with integrity when the ground beneath us may be shifting? How do we center pausing and introspection in an era of AI? And, most importantly: How can we design learning experiences that are as adaptive and resilient as the leaders we serve?

Parece evidente que estamos ante una tormenta perfecta: economía, inteligencia artificial y exceso de formación. La subida del desempleo juvenil (5.8 % en los Estados Unidos) y el aumento del subempleo (más del 40 %), apuntan a que no hablamos tan solo de un problema tecnológico: la economía está ralentizada, las empresas recortan puestos y hay una sobreproducción de graduados. En algunas compañías se están dedicando a pedir a los responsables de equipo que demuestren que la contratación que pretenden conseguir va a desempeñar funciones que una inteligencia artificial no podría llevar a cabo. Esta llamada «elite overproduction» o inflación de credenciales, está amenazando con devaluar los títulos universitarios.

The AFT, alongside the United Federation of Teachers and lead partner Microsoft Corp., founding partner OpenAI, and Anthropic, announced the launch of the National Academy for AI Instruction today. The groundbreaking $23 million education initiative will provide access to free AI training and curriculum for all 1.8 million members of the AFT, starting with K-12 educators. It will be based at a state-of-the-art bricks-and-mortar Manhattan facility designed to transform how artificial intelligence is taught and integrated into classrooms across the United States. 

The academy will help address the gap in structured, accessible AI training and provide a national model for AI-integrated curriculum and teaching that puts educators in the driver’s seat.

Younger generations must be raised to develop their own motivations and goals to prevent their physical and mental atrophy. It is urgent to replace the current productivity-driven education systems with an alternate model that fosters personal growth. This model will preserve children’s innate authenticity and alertness. Providing them with an environment that motivates them to reach their full potential will ensure that children will enjoy a life-long journey of personal growth in a society where AI replaces the need for survival-driven productivity.

Teams today aren’t just asked to execute tasks: They’re called upon to solve problems. You’d think that many brains working together would mean better solutions, but the reality is that too often problem-solving teams fall victim to inefficiency, conflict, and cautious conclusions. The two charts below will help your team think about how to collaborate better and come up with the best solutions for the thorniest challenges.

According to Harvard Business Review the use of GPT is becoming more intimate:

• A significant shift from traditional technical applications to more deeply personal and emotional uses of Generative AI, with custom GPTs enabling tailored solutions in therapy, personal organization, and self-improvement.
• Underscoring the transformative impact of GenAI on daily life and professional environments, demonstrating how effective, user-driven applications are accelerating behavioral change and redefining productivity and support in both personal and business contexts.

¿Ha empeorado la calidad del alumnado en las universidades españolas? Manuel Hidalgo asegura que sí. El profesor universitario ha compartido su hipótesis en su cuenta de X (antes Twitter), donde ha publicado un hilo que trata esta problemática actual.

Young people graduating from college this spring and summer are facing one of the toughest job markets in more than a decade. The unemployment rate for degree holders ages 22 to 27 has reached its highest level in a dozen years, excluding the coronavirus pandemic. Joblessness among that group is now higher than the overall unemployment rate, and the gap is larger than it has been in more than three decades.

La mediación de la IA propicia una ecología cognitiva autoorganizada, donde la disrupción del conocimiento se convierte en un fenómeno fractal, expandiéndose en redes neuronales artificiales y biológicas simultáneamente. En este nuevo paradigma, la interacción docente-estudiante se desplaza hacia un modelo metaheurístico y de autoadaptación algorítmica, integrando la retroalimentación inmediata y el aprendizaje auto-supervisado.

Así, la Nueva Aula se convierte en un pliegue temporoespacial donde convergen realidades paralelas, simuladas y aumentadas, exigiendo nuevas formas de epistemología transdisciplinar que desbordan la racionalidad clásica. Finalmente, el aula del futuro opera como un hipervínculo ontológico, conectando saberes dispersos y agentes cognitivos heterogéneos en una sinfonía inteligente y perpetua, que redefine para siempre el sentido y la función misma de la educación superior.

Employer demand for formal degrees is declining for all jobs, but more quickly for jobs exposed to artificial intelligence, according to the 2025 AI Jobs Barometer report by professional services firm PwC published last week.

“AI helps people rapidly build and command expert knowledge ... which could make formal qualifications less relevant,” according to the report which analyzed close to a billion job ads and thousands of company financial reports across six continents.

This paper introduces "GAIDE: Generative AI for Instructional Development and Education," a novel framework for using Generative AI (GenAI) to enhance educational content creation. GAIDE stands out by offering a practical approach for educators to produce diverse, engaging, and academically rigorous materials. It integrates GenAI into curriculum design, easing the workload of instructors and elevating material quality.

With GAIDE, we present a distinct, adaptable model that harnesses technological progress in education, marking a step towards more efficient instructional development. Motivated by the demand for innovative educational content and the rise of GenAI use among students, this research tackles the challenge of adapting and integrating technology into teaching.

GAIDE aims to streamline content development, encourage the creation of dynamic materials, and demonstrate GenAI's utility in instructional design. The framework is grounded in constructivist learning theory and TPCK, emphasizing the importance of integrating technology in a manner that complements pedagogical goals and content knowledge.

Our approach aids educators in crafting effective GenAI prompts and guides them through interactions with GenAI tools, both of which are critical for generating high-quality, contextually appropriate content. Initial evaluations indicate GAIDE reduces time and effort in content creation, without compromising on the breadth or depth of the content.

Moreover, the use of GenAI has shown promise in deterring conventional cheating methods, suggesting a positive impact on academic integrity and student engagement.

If you’re an instructional designer, you’ve probably noticed how everything has changed over the past few years. What used to take us weeks to create can now be done in days thanks to artificial intelligence. Learners are no longer satisfied with boring PDFs—they want interactive experiences that engage them from the very first moment.

In 2025, instructional design is no longer just about “creating a course.” We now design learning experiences that adapt to each person, learn from their mistakes, and predict what they need before they even know it themselves.

And here’s the challenge: there are so many tools available that it’s easy to get lost. Which tools does an instructional designer really need? Whether you’re designing content for a university, a company, or a training platform, this guide will help you find the best instructional design tools for 2025.

This study explores the neural and behavioral consequences of LLM-assisted essay writing. Participants were divided into three groups: LLM, Search Engine, and Brain-only (no tools).

Each completed three sessions under the same condition. In a fourth session, LLM users were reassigned to Brain-only group (LLM-to-Brain), and Brain-only users were reassigned to LLM condition (Brain-to-LLM). A total of 54 participants took part in Sessions 1-3, with 18 completing session 4. We used electroencephalography (EEG) to assess cognitive load during essay writing, and analyzed essays using NLP, as well as scoring essays with the help from human teachers and an AI judge.

Across groups, NERs, n-gram patterns, and topic ontology showed within-group homogeneity. EEG revealed significant differences in brain connectivity: Brain-only participants exhibited the strongest, most distributed networks; Search Engine users showed moderate engagement; and LLM users displayed the weakest connectivity.

Cognitive activity scaled down in relation to external tool use. In session 4, LLM-to-Brain participants showed reduced alpha and beta connectivity, indicating under-engagement. Brain-to-LLM users exhibited higher memory recall and activation of occipito-parietal and prefrontal areas, similar to Search Engine users.

Self-reported ownership of essays was the lowest in the LLM group and the highest in the Brain-only group. LLM users also struggled to accurately quote their own work. While LLMs offer immediate convenience, our findings highlight potential cognitive costs. Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels. These results raise concerns about the long-term educational implications of LLM reliance and underscore the need for deeper inquiry into AI's role in learning.