Here's How Teachers Should Be Trained To Use AI

Strategies to empower educators.

Many schools aren't treating their teachers right. Not only is the workload too much for them to handle, but the teacher's professional development is either absent or ineffective.

As the Gates Foundation's 2022 State of Education report revealed, over 55% of teachers are dissatisfied with the professional learning opportunities available to them. Yet high-quality, personalized training is precisely what empowers educators and uplifts student outcomes. Without it, mediocrity becomes the norm. 

This issue has only grown more pressing given rapid technological advancements in education. As artificial intelligence and other innovations transform classrooms, teachers need continuous upskilling.

Teacher training can no longer be an afterthought - it must be the cornerstone that enables educators to harness new tools to enhance their expertise.

According to this 2022 OECD report, only 39% of teachers across 32 countries reported adequate preparation in using digital tools. This highlights an urgent need to prioritize teacher training in navigating our AI-driven education landscape.

At present, there is ongoing debate regarding the appropriate role, if any, for AI technologies in education. Some harbour valid concerns about potential risks posed by increased automation and data utilization. However, in my professional assessment, thoughtfully implemented AI solutions should be implemented in education for 2 main reasons:

1. AI makes teaching less stressful

Teacher burnout has reached dire levels, with 44% reporting exhaustion in a recent Gallup survey. Heavy workloads contribute greatly to this unsustainable stress. Administrative burdens and repetitive tasks distract from high-impact instruction and relationship building. This not only diminishes teachers’ job satisfaction but harms student outcomes.

Source

This is where AI presents real solutions. Personalized learning platforms driven by machine learning can take over rote tasks like grading multiple-choice quizzes. Chatbots can handle common student/parent FAQs after school hours. Georgia State University's AI teaching assistant provides such virtual support, reducing instructors' question-answering burden by up to 90%.

Relieving teachers of repetitive tasks enables them to better focus their time on students and themselves. (Because teachers need time for themselves too!). Freed from burnout, educator passion and performance can thrive once more. But successfully leveraging these benefits requires in-depth AI training tailored to teachers’ needs.

2. AI makes teaching more effective

Pre-AI classrooms followed a rigid one-size-fits-all model, unable to accommodate every learner’s needs. But AI analytics uncover previously impossible student insights at scale. Sophisticated algorithms track learning patterns over time, identifying when students struggle or disengage.

Armed with rich data profiles of each student, teachers can gain a deeper view of their strengths, growth areas, interests and motivations. These insights allow more responsive instruction personalized to students’ diverse needs. Early experiments by universities like MIT have shown AI-driven individualization significantly improving student outcomes. 

School leaders must make AI training a top priority and provide teachers with professional development that enables them to evaluate technologies critically, implement them strategically, and direct them compassionately. But how exactly should this be done? Here are 5 strategies you can use:

1. Ground training in learning goals first, not just technology skills. 

For impactful AI teacher training, begin by re-centering the core purpose of education: human development. Have educators clarify the knowledge, critical thinking abilities, and social-emotional aptitudes they aim to instill in students. 

Training should focus on instructional practice to identify key teaching strategies and provide support for implementing those much-needed changes in the classroom.

With this student-centered foundation established, training can then examine whether and how AI may advance – or potentially hinder – pedagogical aims. If specific applications do not serve defined development needs, they should not be deployed simply because they are new and flashy. 

This primacy of human goals and growth ensures technology remains a tool to advance teaching and learning, not an end unto itself. AI training grounded in purpose builds teacher agency rather than overwhelming them with novel tools.

2. Personalize training for each teacher’s unique needs and contexts.

Teacher technology skill levels vary greatly. One-size-fits-all training models often lead many educators to disengage, as they fail to address individual contexts. As Knowles’ Adult Learning Theory explains, professional development must be differentiated, self-directed, and relevant to be truly effective for adult learners.  

Malcolm Knowles’ Adult Learning Theory. Source

Initial surveys can identify teachers’ baseline AI knowledge, existing classroom challenges, and goals. Training is then tiered into manageable steps that progressively build confidence without overwhelming educators.

Content and pace can be adapted to align with teachers’ prior experience and comfort levels. Educators also direct their focus on the aspects most relevant to them. By honouring teachers as autonomous professionals, personalized training breeds much greater motivation, application, and mastery.

3. Focus on active learning through real-world simulations. 

According to Edgar Dale’s Cone of Experience, the least impactful training formats are passive lectures and presentations. Conversely, direct hands-on experience drives the deepest levels of understanding and retention. After covering foundational AI concepts, teachers should create sample AI lesson plans and test AI education tools through simulated classroom scenarios. 

Edgar Dale’s Cone of Experience. Source

For example, they could prototype personalized math learning modules using AI-adaptive software. Teachers would input real (anonymized) student data to tailor the AI content to different learner needs.

This experiential application cements true skills and builds confidence in integrating AI into curricular contexts. Active simulations also facilitate peer critiques and improvement before implementing AI lessons live with students.

4. Embed 1:1 coaching and collaborative sessions.

Research consistently shows one-off lectures or workshops have minimal effectiveness. To solve real classroom challenges, teachers need ongoing support and communities of practice beyond an initial introduction.

Embedding technology coaches who can model AI integration 1:1 provides crucial scaffolding at teachers’ points of need. Coaches introduce tools, co-teach initial AI lessons, observe implementation, and provide feedback. This gradual release of responsibility builds teacher ownership.

Regular peer meetings to co-plan AI usage also build collective capacity and sparks new integration ideas between colleagues. Humans learn best socially. Combining expert guidance with ground-up peer sharing provides impactful, context-specific training tied to real student outcomes.

5. Take an iterative approach with continuous evaluation and improvement. 

Schools should avoid broad AI adoption across all classrooms right away. This is because as I earlier mentioned, AI can be very unpredictable and unreliable. Have teachers first pilot tools with a smaller student group to gather feedback and gauge challenges.

Data and insights from initial small-scale deployments can preemptively uncover potential pitfalls prior to wider implementation. Lessons learned enable schools to refine AI integration processes before scaling them up.

Once launched more widely, teachers and schools must continue evaluating if AI is truly enhancing the student experience and advancing the defined learning goals. Be ready to make ongoing adjustments based on real evidence. Responsible innovation is a continuous process, not a one-time event.

We owe students sustained due diligence as AI capabilities evolve rapidly. But do not let fear of imperfection delay progress - controlled small steps allow us to integrate AI earnestly and ethically.

In a nutshell

Emerging technologies hold the potential to transform classrooms, but only if guided deliberately by knowledgeable, empowered teachers. Thoughtful integration requires investing first and foremost in educator preparation and agency. School leaders have a profound responsibility here. You must make comprehensive, ongoing professional development the cornerstone. 

School leaders who elevate dynamic teacher support will be those best positioned to create transformative learning experiences reflective of our changing world. Proper training determines whether teachers feel threatened, or feel empowered. And teacher mindset directly impacts student outcomes. 

Give educators the freedom to continuously shape and direct how innovations get implemented based on real classroom insights. Adopting the latest tools without elevating teacher expertise will only undermine our ideals.

Let us get back to basics and invest in our teachers. For when we develop their human talents, we ultimately invest in the boundless potential of both students and schools.

Now that you know how to train your teachers on the effective use of AI for teaching and learning, here are 40 AI tools for education you should try:

PS: I am presently offering free consultations and training sessions on implementing AI solutions for educational institutions and companies from now till the end of this month. These pro bono services are available either on-site or online. Do you know anyone who needs these services? If ‘Yes’, kindly copy and share my calendar booking link with them so we can schedule a meeting to plan the training. Everything is 100% free of charge.

Here it is: https://zcal.co/bechemayuk

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