Pedagogy is still a field in continuous innovation, as long as the current pedagogical knowledge constitutes a foundation for new hypotheses and theories, but mostly a tool to understand and appraise new educational practices. In principle, AI comes to empower teachers, to innovate curriculum, to enhance and personalise learning. But how and when are these actually happening? How exactly is this innovation penetrating education theory and practice?

How might we reimagine analog teaching in terms of the digital? (Fyfe, 2011). This is the starting point, with various interpretive openings, from the transposition of education as such in the new environment, to the complete reformulation of learning paths.

In fact, digital pedagogy brings a fresh vision of how to teach and learn, but also of what is taught. The new forms of organization of the learning path sometimes shed a different light on the learning contents, forced to adapt to other dimensions, benefiting from more efficient ways of traversing, interpreting and re-signifying, processing, practicing, internalizing, re-creating, co-creating. Digital technologies facilitate documentation, identification of problems and possible solutions, approach to real life, communication between those involved, structuring of the deconstruction and reconstruction process, contact with experts, finding resources, exposure of results and (public) validation of the approach. But the abundance of possibilities is both an opportunity to use multiple and enhanced elements in the didactic process, as well as a potential obstacle in focusing on relevant learning results and the efficient use of time – conventional pedagogical strategies and established algorithms must incorporate new aspects regarding the selection of relevant and scientifically validated contents, the reinterpretation of the expected learning outcomes, the transposition of work tasks in collaborative (remote) work contexts, ensuring access, techniques to motivate and involve all team members in learning activities, strategies to encourage independent learning, elements of digital security and student safety in the online environment. The vast and unstructured content publicly available in digital format reinforces the educators’ role as facilitator, who has the task, on the one hand, of guiding students to valid sources of knowledge, relevant for the intended learning objectives, and on the other hand part of familiarizing them with ways to identify, evaluate, discern between authentic knowledge/ valuable ideas and alternative sources, unrelated to the corpus of information and values in the widely accepted sphere (Istrate, 2022).

It is important to adopt some best practices when integrating new disruptive technologies such as AI into education:

• Start small: Rather than trying to implement AI across the entire curriculum, it is recommended to start with small steps and work the way up. Identify specific learning objectives or challenges that could be addressed with AI; select appropriate tools or strategies that match your own pedagogical approach; pilot test your ideas with a small group of students before scaling up to larger groups or contexts.
• Collaborate with colleagues: Peers in your university or from other institutions can have similar experiences, and regular collegial discussions can clarify, potentiate, or prevent several issues that might occur.
• Seek feedback and evaluation: Students can be reliable partners in this matter helping you to improve the activity as it unfolds, and providing valuable feedback at the end, so you can adjust learning objectives, content, duration or pace.
• Partner with a reliable AI provider/ Use trustworthy AI tools: Finding reliable AI partners/ systems is crucial for the successful integration of AI. The partner should have expertise in both education and technology domains, as well as a clear vision and mission for using AI for social good. The partner should also provide adequate training, support, and feedback for the educators and learners who use their AI products or services.

The most important benefit of the effort to articulate a digital pedagogy consists in developing the capacity of the teachers to design, carry out and evaluate effective, useful educational situations adapted to the times and to the learners.

“Teaching without digital technology is an irresponsible pedagogy”, said someone at the dawn of digital education (David Parry, 2009, apud Fyfe, 2011), and the meaning of this challenge launched at an early, enthusiastic stage, where technologies showed a lot of potential, is rather an invitation for educators to understand the advantages of new media in order to know when, how, if and why to integrate them into teaching practice. Equally, it can be a warning against unreasonable, limiting conservatism. The subsequent reformulation adequately reflects the concerns of contemporary educational theorists and practitioners: “It is irresponsible to teach with technology without a digital pedagogy” (Fyfe, 2011).

With or without the “digital” addition, pedagogy today incorporates the achievements and promises of technology, in hypothetical scientific extensions in the process of crystallization, sometimes implausible, often valuable, sometimes sterile. It is likely that, after a period of intense searches and rediscoveries, digital technologies will find a place, prominent of course, in an enriched pedagogy and based on new frameworks, in chapters dedicated to teaching methods, external conditions of learning, learning climate, classroom management and others, and in certain situations the education will be digital in a natural way, in a sine qua non cohabitation.

Attempts to limit education to “conventional” spaces and frameworks, in what is called “traditional” education, are a refuge for an anachronistic, reductionist, reality-impassive pedagogy.

Instructional design. Educational activities including AI

In most cases, teachers are turning to new digital tools and resources to organize conventional teaching experiences. The innovation specific to digital pedagogy is where digital educational situations can no longer be transposed (back) into the analogue environment (Istrate, 2022).

Let’s find together some innovative pedagogical elements in AI-assisted educational situations we can design, starting from the following examples.

Example 1. Using IA for Physics

The external photoelectric effect – essay

Learning task for students:

Verify experimentally the laws of the external photoelectric effect, using PhET Colorado simulation:

https://phet.colorado.edu/ro/simulations/photoelectric

Then,

(1) Explore the topic on the Internet and build a short history of research on the external photoelectric effect, in one of the following ways: essay, poster, drawing, demonstration, etc.). Include only the relevant moments of this discovery process.

(2) Use an AI tool to draft a similar essay/ poster/ drawing/ demonstration.

(3) Compare the two outputs and present your conclusions in class.

Intended learning objectives:

To state the laws of the external photoelectric effect

To describe the experimental setup used in the study of the external photoelectric effect

To describe the current-voltage characteristic

To state the laws of the external photoelectric effect

To experimentally highlight the laws of the external photoelectric effect

To verify experimentally (with the help of simulation) the laws of the external photoelectric effect

Expected/ most probable learning path:

Students will access the external photoelectric effect simulation from PhET Colorado. Using the simulation, students will verify each law separately by varying: the flux of incident electromagnetic radiation keeping the frequency constant, then varying the frequency of the radiation, changing the material of the cathodes – all situations mentioned in class.

Then, they will search the Internet for the history of the explanation of the external photoelectric effect and write a short (multimodal) essay on this topic, also reaching Albert Einstein's Photon Hypothesis.

The added value of the workload consists in stimulating conscious learning and reflection on the possibilities offered by advanced technological tools to support the development of scientific fields.

Example 2. Using IA for Career management

Occupations and fields of activity

Learning task for students:

In pairs/ teams: after the class discussion where the most desirable and interesting professional specialisations were determined, one of them will be assigned for each team. The number of members will be approximately equal in all teams formed.

Students will have to trace the professional path of their chosen specialisation. The elements to consider:

1) What exactly does the chosen professional do? (roles, responsibilities, daily tasks);

2) What subjects from your curriculum are useful for following this professional path/ specialisation and to what degree?

3) What competences are the most important?

4) What could be the impact of digital technologies on this profession/ specialisation, in 5/10 years?

5) What career alternatives can be similar to the chosen one? (optional)

Using any digital or physical means (Canva, PowerPoint, physical format - on A3 paper, role play, etc.) students must be able to represent the information found about the proposed topic. For some parts of the task, students can use ChatGPT, Bing Chat or any other AI tool. Students must earmark the ideas provided by AI.

Intended learning objectives:

Students should be able to compare the different fields of activity in order to make informed career decision.

Students should be able to plan their chosen professional path.

Given a model for analysing a professional career/ professional development, the students should be able to distinguish from it the structure of the professional career specific to the job in which they are particularly interested.

Students should be able to apply the tracking scheme of the main activities/ training needed in the evaluation of a professional path (and to predict the professional success).

Expected/ most probable learning path:

Students could solve this task using their creativity and the many digital tools they use even in everyday activities (including AI tools). It should be an interesting activity to students, as it gives more clarity to what their career dreams entail. This can serve both to motivate them and to direct them to activities of interest, but also help them to know alternatives, thus strengthening self-awareness, self-confidence and the idea of safety. In solving the task, students should illustrate/ develop the following abilities: To know some essential criteria for the professional pathway of a job; To plan the chosen professional path; To use online tools and applications to perform various professional tasks; To be able to learn in other ways than the traditional ones, developing their creativity.

Example 3. Using IA for Psychology/ Social sciences

Affectivity – emotions

Learning task for students:

Elaborate a 2 pages paper describing how you imagine a day in your life would go if you felt no emotions at all.

The project will be composed of three parts.

In the first part, answer the requirement using your imagination.

In the second part, use an AI application (eg ChatGPT or another AI application) as a homework helper.

The third part of the project will consist of the opinion/ conclusion you reach by comparing the two versions made in the first two parts.

Intended learning objectives:

To give at least three examples of types of affective processes (affective moods, emotions, feelings, passions) that they can imagine as part of their life experience.

To give examples of physiological changes and behavioural reactions for affective processes such as affective moods or emotions, starting from examples they imagine related to their personal lives.

To identify at least one property of affective processes (expressiveness, conversion, persistence) based on imaginary examples related to their personal life.

To highlight at least one role of affectivity in interpersonal relationships starting from real or imagined life scenarios.

Expected/ most probable learning path:

The requirement is an invitation to students to reflect on the importance of affective processes in their lives. The way the requirement is formulated will allow students to differentiate more easily between a robot (with a large amount of information, but without emotions, consciousness and personal life) and a human who, in addition to having/ understanding some information, is also capable to feel a varied range of emotions that leave their mark on behaviours, decisions and even the way in which information is perceived. Also, the topic allows students to become familiar with AI technology. The specific of the theme focuses on the role of AI in the educational process in order to train students to perceive AI software as potential assistants in the achievement of academic tasks, but that they have the freedom, power and opportunity to complement the help coming from AI with personal input of creativity, analytical power and thus enhance its products obtained with the help of AI.