Digital Rights, Education, and Data: The concept of the “augmented teacher” predates generative artificial intelligence. For years, digital platforms, learning management systems, and assessment tools have helped teachers organize part of their work. But today, new AI tools are no longer limited to storing information, automating tasks, or displaying results. They can generate materials, propose activities, adapt texts to different levels, craft questions, summarize information, or suggest strategies for addressing a specific classroom challenge.
This shift is changing the teacher’s relationship with technology. A traditional platform requires teachers to learn its features and apply them to a specific task. With generative AI, the starting point can simply be a need: “I need to explain this differently,” “make this activity easier,” “suggest three exercises to practice fractions,” or “how can I organize this lesson for students with different reading levels?”
Teachers are already incorporating these possibilities into their work. According to TALIS 2024 (the OECD survey on the state of the teaching profession), approximately one in three teachers uses artificial intelligence in their professional practice within participating education systems. Among them, 68% use it to learn about or summarize information on a topic, and 64% use it to generate lesson plans or activities. Its use is less common in tasks such as assessment or the analysis of student performance, but a pattern is beginning to emerge: AI is first making its way into areas where it can facilitate the preparation and organization of teaching.
Some educational tools are taking this approach a step further. Instead of expecting teachers to find a suitable application for every need on their own, these tools are beginning to bring together different forms of support within a single system. CENTA, for example, offers lesson plan and resource generation alongside professional development content. Other platforms use AI to support assessment, interpret learning data, or adapt activities.
The purpose of these tools is to handle part of a teacher’s workload with the help of an assistant, freeing up more time for other tasks. But to understand what this time savings might actually mean, it’s worth first examining how a teacher’s time is currently allocated.
The work that takes place before and after class
Teaching accounts for only a portion of a teacher’s workday. According to TALIS 2024, full-time teachers devote, on average, about 43% of their work time to teaching itself. Preparing lessons takes up another 14%; grading and evaluating assignments, 9%; and administrative tasks, approximately 6%. More than half of their workday, therefore, is spent on activities other than those directly related to students.
Much of that work is also a significant source of stress. Thirty-five percent of teachers report experiencing considerable or a great deal of stress due to excessive lesson preparation, and 52% cite administrative tasks. It is precisely in this area that generative AI offers its most immediate benefits: preparing a first draft of an activity, crafting questions, adapting a text, or generating examples takes only seconds.
But it’s one thing for a task to be completed more quickly, and quite another for that time savings to be significant in a teacher’s workday. A randomized trial conducted in England by the Education Endowment Foundation and the National Foundation for Educational Research offers some initial insight. The study followed 259 science teachers from 68 secondary schools. One group used ChatGPT, along with a guide, to prepare lessons and materials; the other worked without generative AI. The former spent an average of 56.2 minutes per week preparing the lessons analyzed, compared to 81.5 minutes for the comparison group: 25.3 minutes less, a 31% reduction.
An independent panel also reviewed a sample of the generated materials and found no noticeable differences in quality, although the researchers caution that this part of the analysis was based on a limited sample.
This finding is also interesting because of the way teachers used the tool. ChatGPT was mainly used to generate questions and quizzes, find ideas for activities, or modify existing materials. The AI did not prepare lessons in place of the teacher. It handled small parts of the teacher’s work.
The sum of these small acts of assistance may ultimately prove more significant than the technology’s more spectacular applications. For now, the “augmented teacher” begins by having help to do certain things more quickly. The next step is to have help deciding how to do them.
From Ongoing Training to Ongoing Support
Continuing professional development for teachers has traditionally been organized around courses, workshops, and conferences. Teachers learn a methodology, discover new resources, or receive guidance, and then return to the classroom, where they must apply that knowledge to a specific group and face situations that no course can fully anticipate.
The problem is not only what training they receive, but also when they can access it. TALIS 2024 shows that 63% of teachers cite a lack of time as an obstacle to participating in professional development activities. Nearly 60% mention conflicts with their work schedule, and 46% cite cost.
Researchers have been studying an alternative for years: ongoing support. A meta-analysis of 60 studies with causal designs on teacher coaching found positive effects of this type of support on teaching practices and also—albeit to a lesser extent—on student performance. However, the results diminished as programs expanded and had to serve a larger number of teachers. What works with a coach capable of closely supporting a few teachers is more difficult to replicate for thousands.
Artificial intelligence offers a different possibility: providing some form of professional support when the need arises and doing so on a scale that is difficult to achieve through in-person support.
That is what aprendIA aims to do—a tool developed by the International Rescue Committee for teachers working in vulnerable and crisis-affected contexts. It operates through messaging platforms like WhatsApp and combines short professional development courses with solutions to specific problems related to classroom management, literacy, math, and social-emotional learning.
The questions from teachers who participated in its rollout in northeastern Nigeria help illustrate the difference. They weren’t just asking about broad pedagogical issues. They wanted to know how to manage a large class, regain students’ attention after recess, teach reading, or find another way to explain subtraction. The inquiries were concentrated during the school day and in the afternoon, when they were preparing their lessons.
The first published results are still preliminary. By the end of 2025, 642 teachers had generated more than 124,000 messages in about 10,000 sessions. A comparison of 224 participants before and after the program showed an increase in their reported confidence in adapting lessons to different needs, maintaining students’ attention, and managing large groups. The project has yet to demonstrate whether this use leads to observable changes in teaching practice and, above all, in student learning.
That is precisely what makes this model so valuable. Teachers can complete a short training course on classroom management and then use the same system whenever they need to apply what they’ve learned to a specific situation. Training is no longer just something that happens before teaching; it can now accompany the actual practice.
This is particularly relevant in contexts where education systems lack sufficient trainers, mentors, or specialists to provide ongoing support to all teachers. A phone and a messaging app reduce infrastructure needs and make it possible to provide some form of support even in places where a coach would be difficult to find.
That doesn’t make AI a mentor. A professional can observe a class, get to know the school, interpret relationships, identify challenges the teacher hasn’t articulated, and build a relationship of trust. AI responds to the information it receives. Its value may lie elsewhere: in providing initial support when the immediate alternative is to face the problem without any help.
When the assistant also gives advice
There is a significant difference between asking an AI ten questions about a text and asking it how to work with a student who is struggling to learn to read. As technology shifts from producing materials to recommending pedagogical decisions, our expectations of it must also rise.
A system can design a flawless activity yet propose an inappropriate strategy. It can provide incorrect information, reproduce biases, or recommend practices that ignore the curriculum, language, or cultural context of students. There is also a risk of homogenizing instruction if many teachers rely on the same models to generate activities, explanations, and assessments.
Teachers themselves identify some of these problems. TALIS 2024 shows that seven out of ten believe AI can make it easier for students to pass off someone else’s work as their own, and about four out of ten believe it can amplify biases, reinforce errors, or compromise data privacy and security. At the same time, three out of four say they lack the knowledge or skills needed to teach using artificial intelligence.
The augmented teacher therefore needs more than just tools capable of generating good answers. They need the judgment to decide when to use them, to verify what they produce, and to recognize when a recommendation should be disregarded.
UNESCO places precisely this ability at the center of its AI Competency Framework for Teachers. Its 15 competencies span five areas: 1) a people-centered perspective, 2) AI ethics, 3) fundamentals and applications, 4) pedagogy with AI, and 5) the use of these technologies for professional development. The goal is not simply to learn how to write better prompts, but to understand what the tool can do and to retain responsibility for educational decisions.
The same standard applies to those who design these assistants. Before rolling out aprendIA in Nigeria, for example, the IRC subjected the system to tests designed to detect incorrect answers, tone issues, cultural biases, and security vulnerabilities. The tool also uses educational content that has been pre-selected and adapted to the local context.
Cuando una IA entra en la sala de profesores importa, por tanto, algo más que la calidad del modelo que la sostiene. Importan las fuentes de las que aprende, el currículo que conoce, las lenguas que maneja, los datos a los que accede y los límites establecidos para sus recomendaciones.
Human agency at the center
The term “augmented teacher” might bring to mind an educator capable of producing more: more materials, more exercises, more assessments, and more activities in less time. But to reduce the impact of artificial intelligence to mere productivity would be to miss a significant part of what is at stake. What matters here is what the teacher does with the time and capacity freed up thanks to the help of artificial intelligence.
Observing why a student has fallen behind. Talking to a student who has stopped participating. Adjusting an explanation when the class doesn’t understand it. Recognizing that a carefully prepared activity isn’t working. Listening, asking questions, and improvising are all part of a job that depends on knowing the students and understanding what is happening in the moment.
An AI can generate twenty ways to explain a fraction. The teacher still has to decide which one the child in front of them needs.
UNESCO is already discussing a teacher-AI-student relationship and places human agency at the center: the teacher’s ability to maintain control over decisions and use technology in accordance with pedagogical objectives defined by people.
We still know little about how that relationship will evolve. The evidence regarding time savings is promising but limited. Initiatives such as aprendIA are still being evaluated; and important questions remain regarding quality, dependency, privacy, biases, and effects on professional competence itself.
But AI has already begun to play a role in lesson preparation, assessment, professional development, and the small decisions that surround every hour of teaching. The “augmented teacher” is beginning to emerge. So now we must start thinking about which aspects of their work we want to augment—and for what purpose.
ProFuturo Observatory Team 
