Informal Learning and Cognitive Processes: A Psychological Analysis between Theories and Applications

Informal learning is a key element in the development of knowledge and skills outside formal educational contexts. This study explores the psychological, cognitive, and social dynamics of informal learning, with particular attention to the role of technology. Through a literature review, the processes of self-regulation and metacognition—essential for autonomous learning management—are analyzed, along with the contribution of communities of practice to the co-construction of knowledge. Digital technologies, such as personal learning environments (PLEs), social media, and artificial intelligence, emerge as central tools in facilitating informal learning by expanding access to resources and fostering collaborative interaction. However, challenges related to attention management and overstimulation require targeted pedagogical strategies. Finally, future perspectives are discussed, highlighting the potential of immersive technologies and adaptive systems to personalize and optimize informal learning. This study provides insights for the design of innovative educational environments capable of leveraging the opportunities offered by informal learning in the knowledge society.

Introduction

In an era marked by unprecedented socio-technical acceleration and structural transformations of knowledge production, informal learning is emerging as a key paradigm for understanding the evolving landscape of education. The increasing complexity and fluidity of contemporary life trajectories-intersected by digitalization, labor precarity, and cultural hybridization-require educational systems to expand their conceptual and operational boundaries, embracing forms of learning that are situated, self-directed, and often extra-institutional (OECD, 2021). Far from being peripheral, informal learning constitutes a generative and resilient space where individuals engage in meaning-making processes through everyday practices, digital media, and relational ecologies (Bozaleket al., 2021).

Recent literature emphasizes the strategic value of informal learning within a post-pandemic context, where educational inequalities have been exacerbated, but at the same time, new affordances for personalization, accessibility, and hybridization have emerged. Informal learning-especially when mediated by artificial intelligence, mobile technologies, and networked environments-facilitates flexible and lifelong educational trajectories, enhancing learner agency and adaptability in volatile and uncertain contexts (Kimmons & Rosenberg, 2022).

At the epistemological level, informal learning challenges traditional hierarchies of knowledge, contributing to a broader redefinition of what counts as valuable learning and who gets to be recognized as a legitimate knower (Andreottiet al., 2019). This has important implications for educational equity and epistemic justice, especially when learning takes place outside the institutional curriculum and is rooted in plural, situated, and experiential knowledge systems (Zembylas, 2022).

In this perspective, the integration of informal learning into educational policies and pedagogical practices calls for a radical rethinking of teaching, assessment, and curriculum design-toward more open, inclusive, and participatory models. Moreover, the increasing role of digital technologies requires critical engagement with issues such as algorithmic bias, platform governance, and the datafication of learning (van Dijcket al., 2018), to ensure that the digital mediation of informal learning does not reproduce existing inequalities or marginalize subaltern epistemologies.

This paper explores the theoretical, technological, and pedagogical implications of informal learning within the contemporary knowledge society. Drawing on recent empirical studies and conceptual frameworks, it examines how emerging learning ecologies-supported by AI, immersive environments, and social platforms-can foster personalized and lifelong learning, while also raising crucial questions of inclusivity, recognition, and critical awareness.

Cognitive Plasticity and Informal Learning: Psychological, Social, and Digital Perspectives

Informal learning is a topic of growing interest in psychological and educational research, as it allows for a deeper understanding of how knowledge and skills develop outside formal and structured contexts. Informal learning “[…] is characterized by its flexibility, its situated nature, and its connection with real-life experiences” (Colleyet al., 2017, p. 7). In the context of the digitalization and globalization of education, informal learning environments are increasingly mediated by technologies and social networks, and today also by artificial intelligence (AI), which is redefining the ways knowledge is accessed, used, and constructed. “[…] Digital technologies and social networks have expanded opportunities for informal learning” (Redecker & Punie, 2017, p. 22; Littlejohn & Hood, 2018, p. 5), opening new perspectives but also raising ethical, anthropological, and pedagogical questions.

From the perspective of cognitive and metacognitive processes, “[…] informal learning requires a high degree of self-regulation, in which individuals plan, monitor, and evaluate their own learning without explicit instruction” (Panadero, 2017, p. 2; Bakkeret al., 2021, p. 4). In this view, the central role of personal learning environments (PLEs) and intelligent platforms, increasingly powered by adaptive AI systems, emerges as tools for self-directed learning: “[…] PLEs support self-directed learning by promoting the personalized construction of knowledge through interaction with digital resources” (Dabbaghet al., 2019 , p. 112; Khalil & Ebner, 2020, p. 9).

From a social perspective, informal learning has been analyzed in relation to processes of knowledge co-construction, “[…] learning is seen as a process of co-construction within social contexts” (Sfard, 2018, p. 12; Wenger-Trayner & Wenger-Trayner, 2020, p. 6), highlighting how participation in communities of practice and learning networks fosters skills acquisition. In this context, artificial intelligence can both amplify the possibilities for connection and collaboration and raise new challenges regarding inclusion, representation, and ethical action.

“[…] Digital technologies amplify opportunities for collaborative learning, allowing individuals to access distributed knowledge and actively participate in its construction” (Greenhow & Askari, 2017, p. 630, Czerkawski, 2019, p. 5).

From a neuroscientific perspective, informal learning can influence brain plasticity and long-term memory processes, “[…] informal learning environments influence neural plasticity and long-term memory processes” (Fischeret al., 2019, p. 70, Immordino-Yanget al., 2018, p. 4).

Applied cognitive sciences suggest that “[…] experiential and situated learning promotes greater retention and transferability of knowledge” (Howard-Joneset al., 2021, p. 3). In this framework, educational AI systems may play a relevant role in shaping personalized learning paths, but they also require careful pedagogical reflection on the ethical implications and the protection of subjectivity.

In light of these theoretical and empirical developments, this article aims to explore the psychological and pedagogical dynamics of informal learning in the era of artificial intelligence, analyzing the main cognitive, social, and technological processes that govern its effectiveness and critically questioning the anthropological and ethical impacts of emerging learning models.

Epistemology of Informal Learning: Cognitive, Social, and Digital Dimensions

Informal learning is situated within a multidisciplinary theoretical framework that integrates diverse yet complementary perspectives, such as educational psychology, cognitive sciences, learning technologies, and, more recently, artificial intelligence (AI). Recent literature has emphasized the role of informal learning as a dynamic, distributed, and situated process that occurs in unstructured contexts and increasingly mediated by digital tools and intelligent systems: “[...] informal learning has been described as a dynamic, distributed, and situated process that occurs in unstructured contexts and mediated by digital tools” (Littlejohn & Hood, 2018, p. 22, Greenhow & Askari, 2017, p. 138).

Informal learning has been defined as a spontaneous and unintentional process that occurs outside traditional educational institutions: “[...] informal learning has been described as a spontaneous and unintentional process occurring outside traditional educational institutions” (Colleyet al., 2017, p. 314). Unlike formal learning, which follows a structured curriculum, and non-formal learning, which takes place in organized but uncertified settings, informal learning is often unplanned, emergent, and deeply connected to everyday experience: “[...] informal learning is often unplanned, emergent, and closely tied to daily experience” (Billett, 2019, p. 45).

According to Redecker and Punie (2017), informal learning is distinguished by three fundamental characteristics:

• Situated context: it occurs in natural environments such as the workplace, social interactions, online communities, and AI-based platforms, which today serve as generative spaces of knowledge: “[...] informal learning occurs in natural environments such as the workplace, social interactions, and online communities” (Redecker & Punie, 2017, p. 15).

• Self-regulation and agency: individuals play an active role in managing their own learning, autonomously selecting resources and strategies–even in automated environments: “[...] informal learning requires a high degree of self-regulation, with individuals selecting resources and strategies” (Panadero, 2017, p. 10).

• Technological mediation: digital platforms, social networks, and intelligent systems amplify opportunities for informal learning, enhancing access to distributed knowledge and the personalized construction of knowledge: “[...] digital technologies and social networks amplify opportunities for informal learning” (Greenhow & Robelia, 2019, p. 124).

Informal learning is supported by higher-order cognitive processes such as metacognition, attention regulation, and long-term memory: “[...] informal learning supports higher-order cognitive processes like metacognition and attention regulation” (Fischeret al., 2019, p. 140, Howard-Joneset al., 2021, p. 85). In particular, the concept of self-regulated learning (SRL) is central to the analysis of informal learning. Zimmerman (2002) had already emphasized the importance of self-regulatory strategies, but more recent studies have explored how such strategies adapt to digital and intelligent environments: “[...] recent studies have deepened the understanding of how individuals monitor their learning in digital environments” (Winne & Hadwin, 2018, p. 12, Bakkeret al., 2021, p. 77).

Dabbaghet al. (2019) highlight that the use of personal learning environments (PLEs) allows for advanced personalization of the learning experience through the use of digital resources, blogs, podcasts, and virtual communities. These environments, now enhanced by AI systems, offer new possibilities for adapting to users' cognitive profiles and preferences.

From a neuroscientific perspective, Immordino-Yanget al. (2018) demonstrated that informal learning engages neurocognitive processes integrating emotion and cognition, making learned experiences more meaningful and transferable: “[...] informal learning engages neurocognitive processes that integrate emotion and cognition” (Immordino-Yanget al., 2018, p. 77). Brain plasticity manifests as an adaptive response to environmental, digital, and interactive stimuli, with important implications for lifelong education.

Informal learning is also deeply rooted in social and collaborative dimensions. Wenger-Trayner and Wenger-Trayner (2020) updated the theory of communities of practice, highlighting how participation in professional and social networks fosters the acquisition of skills: “[...] social interaction is fundamental in informal learning” (Wenger-Trayner & Wenger-Trayner, 2020, p. 12). The theory of knowledge co-construction (Sfard, 2018) emphasizes the role of collaboration, now transformed by participatory AI platforms that facilitate distributed and collective forms of learning: “[...] social media facilitate distributed and collaborative learning” (Czerkawski, 2019, p. 50, Khalil & Ebner, 2020, p. 35).

Digital technologies, and particularly artificial intelligence, have profoundly transformed the modalities of informal learning, making it more accessible, interactive, global, and partly automated: “[...] digital technologies have transformed informal learning, making it more accessible, interactive, and global” (Littlejohn & Hood, 2018, p. 60). Today, the digital learning ecosystem includes generative content, recommendation algorithms, and immersive environments that alter traditional knowledge acquisition dynamics.

Greenhow and Askari (2017) analyzed the role of social networks as environments for informal learning, highlighting how tools such as YouTube, Twitter, Reddit, and AI-enhanced environments can develop critical and creative skills. MOOCs, adaptive learning platforms, and intelligent conversational environments represent new frontiers for personalized learning: “[...] MOOCs and adaptive learning platforms offer new opportunities for personalized learning” (Redecker & Punie, 2017, p. 22).

The theoretical framework of informal learning is based on the intersection of cognitive, social, neuroscientific, and technological processes. Contemporary scientific production emphasizes that the effectiveness of informal learning depends on self-regulatory strategies, the quality of social interaction, and the critical ability to use intelligent technologies. Understanding these dynamics is crucial for designing innovative, ethical, and inclusive educational environments capable of enhancing the transformative potential of informal learning in the digital and cognitive society of artificial intelligence.

Informal Learning in the Digital Age: A Comparison Between Self-Regulatory, Interactional, and Technological Models

To understand the psychological dynamics of informal learning, it is necessary to consider the complex interaction between cognitive, social, and technological processes, as highlighted by the most recent scientific literature. The theoretical framework of self-regulated learning (SRL) proves particularly useful for analyzing how individuals autonomously manage their learning paths in informal contexts. Panadero (2017, p. 5) pointed out that “[...] students with greater self-regulation skills tend to better exploit the resources of informal learning, developing planning and monitoring strategies.” In confirmation of this, Bakkeret al.(2021, p. 240) explored the role of metacognition in digital learning, emphasizing how it constitutes a fundamental lever for the control and adaptation of cognitive behavior in informal environments.

The comparison between the studies of Winne and Hadwin (2018, pp. 41–43) and Dabbaghet al. (2019, pp. 1–3) allows for a deeper understanding of the influence of digital environments on self-regulated learning. While the former focus on the interaction between self-regulation and working memory in online contexts, the latter analyze the potential of personal learning environments (PLEs) to promote personalized and self-directed learning paths. In both cases, it clearly emerges that “[...] the effectiveness of informal learning is closely linked to individuals’ ability to autonomously manage their learning process.”

From a neuroscientific perspective, studies such as those by Fischeret al. (2019, pp. 112–113) and Immordino-Yanget al. (2018, p.45) have shown that informal learning activates deep cognitive processes and promotes brain plasticity. In particular, “[...] informal learning stimulates deep cognitive processes, fostering brain plasticity and the connection between emotions and learning.” However, while Fischer et al. emphasize experiential learning and memory consolidation, Immordino-Yang et al. highlight the integration between emotion and cognition as a key factor for the meaningfulness of the experience.

Informal learning also appears as a process strongly influenced by social interactions. Wenger-Trayner and Wenger-Trayner (2020, p. 13) and Sfard (2018, p. 74) have emphasized how communities of practice and the processes of co-construction of knowledge are central to skills acquisition in informal contexts. A comparison between these contributions and the study by Czerkawski (2019, p. 76) highlights complementary perspectives: on one hand, learning through active engagement in social and professional contexts; on the other, the expansion of collaborative learning opportunities through social media. In particular, Greenhow and Robelia (2019, p. 135) point out that platforms such as Twitter and Reddit facilitate knowledge sharing, stimulating discussions among users with varying levels of expertise.

Also significant is the comparison between the situated learning theory of Lave and Wenger (1991, p. 29), which emphasizes the value of local context and legitimate peripheral participation, and the studies by Khalil and Ebner (2020, p. 15), which show how Massive Open Online Courses (MOOCs) create informal learning environments on a global scale. “[...] MOOCs overcome geographical barriers, offering informal learning experiences to a global community,” demonstrating the possibility of replicating some dynamics typical of communities of practice even in virtual and transnational environments.

Digital technologies have redefined informal learning, expanding its accessibility and personalization. The studies by Redecker and Punie (2017, p. 6) and Littlejohn and Hood (2018, pp. 56–57) offer complementary interpretations. While the former focus on the impact of institutional educational technologies, such as LMS platforms, the latter analyze the informal use of digital tools (blogs, social networks) for self-constructed knowledge building. In both cases, it emerges that technologies can support personalized and flexible learning paths, although they require high levels of self-regulation.

A further perspective is offered by the comparison between Greenhow and Askari (2017, p. 111), who analyze the collaborative potential of social media, and Howard-Joneset al. (2021, pp. 18-19), who warn about the risks associated with cognitive overstimulation. “[...] While Greenhow & Askari highlight the collaborative potential of digital networks, Howard-Jones et al. warn against the negative effects of overstimulation and distraction in digital contexts,” suggesting a conscious and critical use of technologies to avoid dysfunctional effects on attention and memory.

In this context, the research byBakkeret al. (2021, p. 37) also fits, investigating the integration of artificial intelligence (AI) into informal learning. The results show that “[...] although AI can facilitate access to information, the role of human guidance remains fundamental for the development of critical thinking and metacognitive skills.” The use of educational chatbots and virtual tutors can thus represent an opportunity, but not a substitute for meaningful educational interaction.

The comparative analysis of the research highlights how informal learning is a complex, multidimensional, and situated process, intertwining cognitive, social, and technological variables. In particular, it emerges that:

• “[…] Self-regulated learning and the use of metacognitive strategies are essential to best exploit the opportunities of informal learning” (Panadero, 2017, p. 5; Winne & Hadwin, 2018, p. 41);

• “[…] Communities of practice and the co-construction of knowledge play a key role in social learning processes” (Wenger-Trayner & Wenger-Trayner, 2020, p. 13; Czerkawski, 2019, p. 76);

• “[…] Digital technologies expand learning opportunities but present challenges related to attention management and interaction with AI” (Greenhow & Askari, 2017, p. 11; Howard-Joneset al., 2021, p. 18).

These findings suggest the opportunity to design innovative educational environments that integrate advanced technological tools, enhance social interaction, and support the development of metacognitive skills. Only a holistic and integrated approach can maximize the potential of informal learning within the knowledge society, promoting inclusive, autonomous, and meaningful learning paths.

Informal Learning and Educational Transformations: A Prospective View between Personalization, Equity, and Lifelong Learning

In light of the emerging evidence, the future of informal learning appears increasingly oriented toward a synergistic integration of technological innovation, personalization of learning pathways, and pedagogical approaches geared toward flexibility. The growing relevance of this educational paradigm reflects the transformations of the knowledge society, which demands continuous, adaptable, and learner-centered learning modes.

Artificial intelligence (AI) is positioned as one of the main catalysts of this evolution. Several studies (Bakkeret al., 2021; Luckin, 2020) highlight the potential of educational chatbots, intelligent tutors, and adaptive environments in optimizing self-directed learning processes, offering real-time personalized support and enhancing users' metacognitive abilities.

At the same time, the adoption of immersive technologies such as augmented reality (AR) and virtual reality (VR) is opening new frontiers for informal learning, enabling experiential and interactive experiences. Research by Dede (2021) and Radiantiet al. (2020) shows how the use of realistic simulations fosters the acquisition of skills applicable in real-world contexts, increasing engagement and facilitating knowledge transfer.

An emerging trend concerns the development of personalized learning environments, powered by machine learning algorithms capable of dynamically adapting to individual learning needs. In this regard, Redecker and Punie (2017) envision scenarios where such systems could select resources, monitor progress, and provide formative feedback, transforming access to knowledge into an increasingly individualized and effective experience.

Informal learning also plays a crucial role in the development of transversal skills required in contemporary society. According to Greenhow and Robelia (2019) and Khalil and Ebner (2020), interactions on social media and in digital communities foster the emergence of abilities such as problem-solving, critical thinking, and effective communication, which are particularly relevant in rapidly changing professional contexts.

Another significant development is the growing institutionalization of informal learning through certification devices such as micro-credentials and open badges. Studies conducted by Colleyet al. (2017) and Littlejohn and Hood (2018) confirm the value of these tools in facilitating the transition to the labor market and promoting the validation of skills acquired outside traditional educational settings.

Inclusivity represents another strategic frontier. Digital technologies offer opportunities for access to learning even for individuals excluded from formal education circuits due to socio-economic or geographical reasons. Analyses by Czerkawski (2019) highlight how online platforms can contribute to reducing the educational gap by offering free and globally accessible resources. However, this potential is conditioned by the ability of educational systems to address the digital divide, which remains significant in many areas of the world.

Learning communities will continue to play a central role, understood as collaborative interaction spaces capable of promoting the co-construction of knowledge. Research by Wenger-Trayner and Wenger-Trayner (2020) shows how these contexts, both digital and face-to-face, can contribute to social integration and meaningful learning, especially for individuals from diverse cultural backgrounds.

Nonetheless, some critical issues persist. The quality and reliability of learning sources represent a challenge in a context marked by information abundance. Greenhow and Askari (2017) highlight the difficulty many users face in evaluating the credibility of online resources. Furthermore, the institutional recognition of informal learning still requires the definition of standardized certification criteria. Finally, as observed by Howard-Joneset al. (2021), an excess of digital stimuli can lead to cognitive overload and compromise attention capacity and knowledge consolidation.

Looking ahead, the emergence of a hybrid educational ecosystem seems likely, based on cooperation between traditional institutions, digital platforms, enterprises, and communities of practice. In this context, informal learning will be valued within flexible and modular training pathways. As Littlejohn and Hood (2018) emphasize, open and participatory educational models, based on knowledge sharing through networks, will play an increasingly significant role in the architecture of future education.

Finally, the principle of lifelong learning will be consolidated as an indispensable reference point. Lifelong learning, supported by digital tools and adaptive environments, will become a necessary condition to face labor market instability and rapid technological changes. Reflections by Dede (2021) confirm that adaptability and the ability to learn continuously are key competencies for personal and professional fulfillment in the era of complexity.

Informal Learning in the Knowledge Society: Theoretical Overview and Operational Pathways

In the knowledge society, informal learning emerges as an essential component for addressing the instability of knowledge, the fluidity of professional trajectories, and the urgency of lifelong learning. Far from being marginal, it represents a powerful form of skills acquisition, often transversal, taking place in everyday contexts, digital flows, participatory practices, and collaborative processes (Livingstone, 2011; Sangrà et al., 2012).

Recent theoretical developments propose interpreting informal learning through the lens of connectivism (Siemens, 2014), which highlights how knowledge is distributed across a network of nodes (both human and non-human), and how the fundamental competence lies in the ability to navigate, select, and create connections within complex informational systems. This perspective is particularly relevant in the context of AI, which acts both as an environment and an agent within the learning ecosystem, with an increasing influence on cognitive and decision-making processes (Luckin, 2017; Eynon & Malmberg, 2021).

The ecological approach to informal learning has also received new formulations: authors such as Thomas and Seely Brown (2011) speak of emergent learning environments, where knowledge is collaboratively constructed through open, distributed, and often informal social practices that arise even in extra-institutional contexts and digital media. Transformative learning theory has been updated by Mezirow based on recent empirical research, recognizing the growing role of informal learning in triggering profound changes in meaning perspectives, especially in contexts of crisis or transition (Mezirow & Taylor, 2011).

Educational technologies have made informal learning increasingly traceable, fostering the emergence of micro-learning, personalized learning pathways, and forms of peer learning mediated by social platforms and intelligent environments. However, this also brings new challenges, such as algorithmic opacity, the risk of the datafication of education (Williamson, 2017), and the need to develop critical and digital citizenship (Hinrichsen & Coombs, 2013).

For a pedagogy sensitive to informal learning, it is necessary to develop mechanisms capable of:

• recognizing and validating informal knowledge through tools such as digital badges, open credentials, and validation systems for competencies acquired on-the-job or in contexts of volunteering and civic participation (Redecker, 2017);

• designing blended environments that integrate formal and informal experiences (Anderson, 2020);

• promoting narrative and self-reflective practices such as digital storytelling, allowing learners to construct and share meaningful representations of their learning experiences (Robin, 2016);

• strengthening the role of educational communities and local territories, also in the perspective of transformative education and social justice (Biesta, 2019).

Finally, a critical rethinking of traditional assessment paradigms is needed, introducing ecological approaches to learning evaluation (Ferguson et al., 2019), capable of capturing the complexity, non-linearity, and relationality of individual learning pathways in the hyperconnected society.