This sounds like much work, but these ten competencies may serve as a light on the hill for professional development on the “jaded edge” of AI in education. This is a noisy space, and an excellent way to move forward is through developing new skills and augmenting hard-earned older ones in critical areas. I will refine this list and link to resources over the coming weeks. A lot of this work can be co-designed with learning designers and academic developers, so don’t stress.
1. AI Literacy
Teachers need to develop a comprehensive understanding of AI tools, focusing on their capabilities and limitations with a nuanced perspective suitable for their discipline. This understanding goes beyond basic usage; educators must critically evaluate AI-generated content. It is essential for teachers to master AI platforms for teaching, learning, and assessment while also creating effective strategies for teaching ethical AI engagement. Developing AI literacy involves transforming technological tools into meaningful pedagogical instruments that enhance, rather than replace, human learning experiences.
2. Digital Competence
Digital competence has evolved from a supplementary skill to a fundamental teaching requirement. Modern educators must demonstrate proficiency across multiple digital platforms, creating interactive and engaging learning environments. This competency involves understanding technological tools’ pedagogical potential, from collaborative online platforms to generative systems. Teachers should be able to seamlessly integrate digital technologies, design technology-enhanced activities, and support students’ digital skill development (with the help of a good LD). The goal augments teaching approaches and creates dynamic, networked learning experiences.
3. Assessment Design and Literacy
Assessment design has become a critical challenge in the AI era. Educators must develop sophisticated, AI-resistant assessment strategies that prioritise authentic learning evaluation (or ‘valid’ assessments). This involves designing tasks that assess process over product, incorporating evaluative judgment, creating nested or staged assessments, and diversifying assessment formats. Teachers must understand how to develop context-specific assignments that require personal reflection and critical thinking and demonstrate genuine understanding. Strategies include incorporating more in-class assignments, group work, and oral interviews that test deeper comprehension beyond surface-level knowledge reproduction. These can be done either with or without AI (the two-lane approach).
4. Critical Thinking and Pedagogical Adaptation
In an AI-driven educational landscape, fostering critical thinking becomes paramount. Teachers must design learning experiences that encourage students to evaluate information critically, recognise potential AI-generated biases, and develop complex problem-solving skills. This competency requires adopting constructivist and connectivism learning approaches, emphasising knowledge construction through social interaction and networked learning. Educators should create pedagogical environments encouraging students to question, analyse, and synthesise information, transforming AI from a potential threat to a collaborative learning tool.
5. Ethical Awareness and Decision-Making
Ethical considerations are central to responsible AI integration in education. Teachers must develop a nuanced understanding of AI’s ethical implications, including data privacy, algorithmic bias, and fairness. This competency involves modelling ethical technology use, guiding students in responsible AI engagement, and maintaining human agency in technological interactions. Educators should critically examine AI tools’ potential societal impacts, encourage transparent discussions about technological ethics, and help students develop a robust ethical framework for navigating increasingly complex technological landscapes.
6. Focus on Soft Skills Development
As AI advances, human-centred skills become increasingly valuable. Teachers must prioritise developing essential soft skills like empathy, communication, emotional intelligence, and cultural competence. These skills transcend technological capabilities and focus on human interaction, interpersonal understanding, and adaptive social capabilities. Educators should create learning environments that nurture patience, self-awareness, and nuanced communication. By emphasising these human qualities, teachers prepare students for professional environments where emotional intelligence and interpersonal skills remain irreplaceable.
7. Communication and Collaboration
Effective communication and collaboration skills are crucial in AI-enhanced educational environments. Teachers must facilitate meaningful interactions across digital and physical platforms, creating collaborative learning networks. This competency involves mastering various communication technologies, designing group activities that promote knowledge co-construction, and supporting students in developing teamwork skills. Educators should leverage connectivist learning theories, encouraging students to learn through networked interactions and diverse perspectives, transforming communication from a unidirectional to a multidimensional experience.
8. Lifelong Learning and Adaptability
In a rapidly evolving technological landscape, continuous learning becomes essential. Teachers must cultivate a growth mindset and stay updated on emerging AI technologies, pedagogical innovations, and educational research. This competency involves proactively seeking professional development, experimenting with new teaching approaches, and maintaining curiosity about technological advancements. Educators should model lifelong learning for students, demonstrating adaptability, resilience, and enthusiasm for ongoing skill development in an increasingly dynamic educational environment.
9. Personalised Learning and Support
AI offers unprecedented opportunities for personalised learning. Teachers must understand how to leverage AI technologies to create individualised learning experiences, adapting content, pace, and support to each student’s unique needs. This competency may involve using adaptive learning approaches or intelligent tutoring systems (ones that don’t just give the right answer). Educators should balance technological personalisation with human empathy, ensuring AI is a supportive tool rather than a replacement for nuanced, compassionate teaching.
10. Understanding of Learning Theories and Research
A robust theoretical foundation remains crucial in AI-integrated education. Teachers must ground their practice in established learning theories like constructivism, social constructivism, and connectivism. This competency involves critically analysing how AI intersects with pedagogical approaches and understanding learning as an active, socially constructed process. Educators should be able to evaluate AI tools through theoretical lenses, ensuring that technological integration enhances rather than undermines fundamental educational principles.
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