Digital Pedagogy provides new possibilities to support learning
The use of digital tools and environments in education enables, in addition to easy access to information and learning materials, the adoption of new pedagogical methods and practices. These may include collaborative knowledge building, multimodality (the use of various media elements such as images, animations, audio, and videos), and personalization of learning—adaptation to learners’ individual needs and offering optimal support for those with language challenges or learning disabilities.
Educators have different means of fostering learning with digital tools compared to traditional face-to-face instruction. This distinction is often characterized as follows: Traditional face-to-face teaching is based on the teacher’s teaching process, whereas digital learning is based on the learner’s learning process. Educators influence learners’ learning processes primarily through the methods and activities they choose. Selecting appropriate teaching and learning methods requires an understanding of how humans learn and process information.
In the following section, examples are provided on how various pedagogical models can be utilized in digital learning and teaching, whether online, face-to-face, or in a blended learning environment. (Generative AI has been employed as a supportive tool in developing the following descriptions and examples.)
Digital Pedagogy Models
Problem-based learning | Progressive Inquiry -Based Learning | Flipped Classroom | Competency-Based Learning | Experiential Learning | Design-Based Learning| | Phenomenon-Based Learning | Portfolio Learning
Overview
Problem-Based Learning is a student-centered model in which learners acquire knowledge by engaging with relevant problems. Instead of passively absorbing information, students work collaboratively to build new knowledge and determine how they can solve the problem. Teachers act as facilitators, guiding the process rather than delivering information.
Use in the Age of AI
AI tools can serve as powerful resources for PBL. Students may use AI-driven data analysis, simulations, or conversational assistants to generate ideas, test hypotheses, or evaluate solutions. AI tools can provide adaptive scaffolding. At the same time, PBL with AI highlights the importance of critical engagement: students must learn to question the accuracy, bias, and reliability of AI-generated outputs.
Benefits
- Develops collaboration, problem-solving, and transversal skills.
- Promotes epistemic fluency by teaching students how knowledge is constructed and validated.
- Encourages learners to evaluate AI as a problem-solving partner critically.
Example
In a science class, students explore the problem: “How can we reduce the school’s carbon footprint?” They use AI tools to analyze energy consumption data and simulate the effect of various solutions. The teacher guides them to question the assumptions underlying the AI model and reflect on the reliability of the data.
Overview
Inquiry-Based Learning is a learner-centered pedagogical model in which learning is built on questioning, investigation, and discovery. It aims to develop learners’ critical thinking, knowledge-building skills, and curiosity. The model follows a cyclical process, where learners formulate a problem or a guiding question and construct initial explanations or working theories. Guided by these theories, they search for new information, test and revise their ideas, and gradually deepen their understanding. Students work in teams, formulating their own research questions, collecting and analyzing information, and drawing conclusions. The teacher’s role is to guide the process, support the formulation of questions, and help evaluate the reliability of information.
Using AI in Inquiry-Based Learning
AI tools can serve as powerful resources for Inquiry-Based Learning. Students may use AI-driven data analysis, simulations, or conversational assistants Artificial Intelligence provides new tools and opportunities to support inquiry-based learning. Students can use AI for:
- developing and refining research questions (e.g., using conversational AI as a thinking partner),
- searching for and classifying information sources,
- analysing and visualising data,
- testing hypotheses through AI-powered simulations,
- reflecting on and assessing their own learning using AI-based feedback tools.
It is essential that AI acts as a support tool, not a source of truth. Students learn to evaluate how AI generates information, identify its limitations and biases, and understand how it differs from human scientific reasoning. This fosters epistemic understanding—the ability to understand how knowledge is generated, validated, and assessed.
Benefits
- Strengthens critical thinking and investigative approaches to learning.
- Helps students understand how AI works and how knowledge is constructed.
- Supports learner agency and active knowledge building.
Example
In an upper secondary biology class, students investigate the phenomenon “How does urbanization affect the number of pollinators?” They use AI tools to collect information from various sources, classify data, and create graphical representations. AI may suggest hypotheses or detect trends in the data, but it is the students themselves who interpret and evaluate the significance and reliability of the results.
Overview
The Flipped Classroom is an instructional model that inverts the traditional approach to teaching and learning. Instead of focusing on the direct delivery of content during classroom sessions (or virtual online video sessions), instructional materials—such as online videos, readings, or other multimedia resources—are accessed by students outside of class, often at their own time and pace. This shift allows classroom time to be dedicated to active engagement, collaborative tasks, problem-solving, and guided practice. By transferring the initial knowledge acquisition process outside the classroom, learners come prepared to engage in deeper discussions and apply concepts in meaningful contexts.
Use in the Age of AI
The Flipped Classroom, combined with AI, offers both flexibility and enhanced opportunities for personalized, data-informed, and active learning. AI can be used to deliver personalized content before class, or students may use AI to create summaries on the topics before class.
Benefits
- Maximizes classroom interaction time.
- Provides opportunities for personalized learning outside class.
- Enhances teachers’ capacity to identify learning gaps.
Example
In mathematics, students use an AI tutoring system at home to learn about quadratic equations. The system adapts exercises to each learner. In class, students work in groups on real-world applications, while the teacher uses AI dashboards to identify who needs extra support.
Overview
Competency-Based Learning (CBL) is an instructional approach that emphasizes the mastery of clearly defined skills, knowledge, and attitudes—referred to as competencies—rather than the completion of standardized course content or fixed time requirements. CBL focuses on the mastery of clearly defined competencies, optimally allowing learners to progress at their own pace once mastery is demonstrated. Learners are encouraged to progress at their own speed, which allows them to spend more time on challenging areas while accelerating through concepts they grasp quickly. Assessment is ongoing and often integrated into the learning process, using performance-based tasks, portfolios, and real-world applications as evidence of mastery. Teachers, in this model, shift from being content deliverers to mentors and assessors, guiding learners in setting goals, monitoring progress, and applying their knowledge in authentic contexts.
Use in the Age of AI
AI assessment systems can track micro-skills, provide adaptive exercises, and generate personalized feedback. Predictive analytics can identify struggling students early. Teachers remain essential in assessing higher-order competencies such as creativity and ethics.
Benefits
- Personalizes learning pathways.
- Supports data-driven insights into learner progress.
- Aligns learning outcomes with real-world digital skills.
Example
In language learning, an AI platform with Learning Analytics tracks students’ vocabulary acquisition. Learners progress once they demonstrate mastery, while the system recommends tailored activities. Teachers provide feedback on broader skills such as conversation and cultural context.
Overview
Experiential Learning is a pedagogical model that emphasizes learning through direct experience, combined with cycles of reflection, conceptualization, and application. The approach is based on the idea that learners construct knowledge most effectively when they actively engage in meaningful experiences, think critically about them, form generalizations, and then test those insights in new situations. This cyclical process creates continuous opportunities for deep learning, skill development, and personal growth. In practice, experiential learning often involves activities such as simulations, fieldwork, project-based tasks, or real-world problem solving.
Use in the Age of AI
AI-driven simulations, VR, and AR environments can provide immersive experiences, such as medical training or climate change scenarios. Learners reflect on both their own actions and the AI system’s responses, building critical awareness of the technology’s affordances and limitations.
Benefits of Digital Pedagogy
- Supports active, hands-on learning.
- Enables experiences that would be otherwise impossible in school settings.
- Promotes reflective awareness of AI as a learning partner.
Example
In civics, students participate in an AI-driven role-playing simulation that simulates parliamentary debates. They reflect on how AI-generated arguments influenced the discussion and consider the ethical dimensions of automated speech.
Overview
Design-Based Learning (DBL) is a pedagogical model that actively engages learners in iterative cycles of designing, testing, and refining solutions to open-ended, authentic problems. At the heart of this approach is the idea that knowledge and skills are best developed when students create tangible products, systems, or processes in response to real or simulated challenges. By situating learning in design tasks, DBL fosters creativity, critical thinking, and problem-solving abilities while encouraging students to integrate knowledge from multiple disciplines.
Use in the Age of AI
AI can support prototyping, modeling, and creativity. For example, students can use AI design tools to test solutions or generate alternative design options. Teachers ensure students critically question design decisions and consider ethical and sustainability implications.
Benefits
- Cultivates innovation, resilience, and experimentation.
- Provides authentic contexts for applying AI tools and simulations.
- Integrates ethical reflection into design processes.
Example
In technology education, students design a sustainable smart home model. They use AI to optimize energy efficiency and simulate usage. The class then discusses ethical issues such as data privacy.
Overview
In practice, learners work collaboratively to investigate phenomena from multiple angles, drawing on knowledge and methods from different fields. This process often begins with formulating inquiry questions, followed by collecting and analyzing information, and synthesizing insights into a coherent understanding of the phenomenon. Teachers act as facilitators, guiding the inquiry, supporting critical reflection, and helping students connect their learning to broader social, cultural, and ethical contexts. Rather than focusing narrowly on factual recall, PhenoBL emphasizes developing transversal skills such as critical thinking, problem-solving, collaboration, and systems thinking.
Use in the Age of AI
AI offers powerful opportunities for enriching PhenoBL by enabling students to access, analyze, and synthesize large and complex datasets. AI tools can support learners in:
- Generating cross-disciplinary connections by suggesting relevant sources from diverse fields.
- Visualizing complex phenomena through simulations or AI-driven data modeling.
- Assisting with multilingual sources, allowing students to include global perspectives.
- Supporting reflection by providing alternative interpretations or prompting critical questions.
Example
In a secondary school PhenoBL module on “The Future of Work,” students explore how automation and AI are changing employment, society, and education. They use AI-powered tools to analyze labor market data, generate visualizations of emerging job trends, and simulate possible future scenarios.
In groups, students combine perspectives from economics, ethics, technology, and social sciences to propose strategies for inclusive and sustainable employment. Teachers facilitate discussions on ethical questions such as fairness, bias, and the societal impact of automation.
Benefits
- Promotes interdisciplinary and holistic understanding of real-world issues.
- Helps students critically integrate AI as both a learning partner and a subject of inquiry.
- Builds collaboration, critical reflection, and creativity.
- Strengthens global citizenship and ethical awareness in AI-mediated societies.
Overview
Portfolio Learning is a pedagogical model in which students collect, curate, and reflect on their work over time to demonstrate progress, skills, and achievements. Unlike traditional testing, which offers only a snapshot of performance, portfolios provide a comprehensive view of the learning process by capturing development, growth, and self-reflection throughout the learning process. A portfolio is, first and foremost, a tool through which the student’s learning and development process can be documented and made visible, both for the student’s own reflection and for teachers, peers, or external evaluators.
The model supports learners in developing a deeper awareness of how they learn, encouraging metacognition and responsibility for their own learning journey. By selecting and commenting on meaningful artifacts—such as essays, projects, creative work, or other digital products—students engage in a process of critical reflection, identifying strengths, areas for improvement, and evidence of growth over time. Portfolios can be used for both formative assessment, supporting learning through continuous feedback and reflection, and for summative assessment, evaluating outcomes and demonstrating the competencies achieved. Teachers act as mentors, guiding learners in setting goals, selecting relevant artifacts, and engaging in reflective dialogue about their progress.
Use in the Age of AI
AI technologies expand the possibilities of portfolio learning by:
- Supporting students in self-reflecting on their learning and curating digital artifacts (texts, videos, designs, code, etc.).
- Offering personalized feedback on portfolio entries, highlighting strengths and areas for improvement.
- Helping learners analyze their own progress through AI-powered analytics (e.g., tracking writing development or problem-solving skills).
At the same time, educators must ensure that AI feedback does not replace authentic learner reflection. Instead, AI should act as a scaffolding tool that enhances learners’ metacognitive skills and ability to evaluate their own work.
Example
In a language learning course, students create a digital portfolio that includes essays, audio recordings, and video presentations. AI writing assistants provide suggestions for grammar and vocabulary, while speech analysis tools give feedback on pronunciation. Over the semester, students use AI-generated analytics to observe improvements in fluency and complexity. Teachers encourage students to critically reflect on both their performance and the AI feedback, integrating personal insights into their portfolio.
Benefits
- Encourages ownership of learning and provides a platform for long-term reflection and growth.
- Supports formative assessment with AI-powered analytics and feedback.
- Develops digital literacy through curating multimodal artifacts.
- Promotes metacognition by combining human reflection with AI feedback.