Is Lifelong Learning Sustainable with EdTech?

 

A Sociotechnical and Interpretivist Analysis of Neurodiverse Learning in AI-Mediated Contexts

Abstract

The expansion of educational technology (EdTech) has reinforced lifelong learning as both an economic necessity and a social expectation within contemporary knowledge societies. Nevertheless, the sustainability of lifelong learning facilitated by EdTech remains under-theorised, especially regarding neurodiverse learners and the growing influence of artificial intelligence (AI) in educational environments. This paper addresses this gap by employing an interpretivist and sociotechnical framework to analyse how sustainability is constructed, experienced, and governed within AI-mediated EdTech ecosystems. Drawing on recent literature (2020–2025), sustainability is conceptualised across three interrelated dimensions: cognitive, social, and infrastructural. While EdTech offers flexible, personalised, and scalable learning opportunities, it also introduces new forms of cognitive burden, motivational fragility, and data-driven governance that disproportionately impact neurodiverse learners. This paper contributes a critical perspective by reframing lifelong learning as a sociotechnical regime that shifts responsibility for continuous adaptation onto learners, rather than as an inherently beneficial educational paradigm. Sustainability is thus presented not as an intrinsic property of EdTech systems, but as an emergent condition shaped by inclusive design, ethical data practices, and socially embedded learning ecologies.

Introduction

Lifelong learning has become a central organising principle of contemporary education policy, increasingly framed as essential for economic competitiveness, workforce adaptability, and personal development (OECD, 2021; World Bank, 2022). The rapid proliferation of educational technology (EdTech), including AI-driven platforms, adaptive learning systems, and micro-credential ecosystems, has further intensified this framing by promising continuous, flexible, and personalised learning opportunities across the lifespan (Holmes et al., 2022; Zawacki-Richter et al., 2023).

Despite widespread enthusiasm, a critical gap persists. Although existing research has explored access, engagement, and personalization in EdTech, limited attention has been devoted to how neurodiverse learners experience and negotiate the sustainability of lifelong learning within data-driven, AI-mediated environments. This omission is significant because neurodiversity challenges prevailing assumptions regarding standardized cognition, linear progression, and continuous productivity (Walker, 2021; Milton, 2020).

This paper addresses this gap by asking:

1. How is the sustainability of lifelong learning constructed within EdTech ecosystems?
2. How do neurodiverse learners experience and interpret these systems over time?
3. What sociotechnical and AI-driven factors shape the sustainability of engagement?

Adopting an interpretivist paradigm, the paper conceptualises sustainability not as a measurable outcome (e.g., completion rates), but as a lived, evolving experience shaped by the interaction of cognitive demands, social contexts, and technological infrastructures. It argues that lifelong learning through EdTech is not inherently sustainable; rather, sustainability emerges only under specific sociotechnical conditions.

Theoretical Framework

Interpretivism and the Lived Experience of Learning

Interpretivism foregrounds the subjective meanings individuals attach to their experiences (Schwandt, 2014). In contrast to positivist approaches that prioritise quantifiable outcomes, this perspective emphasises how learners make sense of engagement, motivation, and inclusion over time.

This focus is particularly important for neurodiverse learners. Variations in cognitive processing, sensory sensitivities, and alternative attention patterns influence how learning environments are experienced (Milton, 2020). Consequently, sustainability should be understood as a relational and experiential phenomenon rather than a fixed property of educational systems.

A Sociotechnical Perspective on EdTech

EdTech systems are not neutral tools but components of broader sociotechnical assemblages that include institutional policies, platform architectures, cultural norms, and economic imperatives (Selwyn, 2022; Fawns et al., 2023). Lifelong learning is thus co-constructed through:

• Platform design and algorithmic systems
• Institutional expectations of continuous upskilling
• Labor market pressures
• Cultural narratives of productivity

This perspective challenges techno-solutionist assumptions and highlights the structural conditions shaping learning sustainability.

Neurodiversity as a Critical Lens

Neurodiversity conceptualizes neurological differences as natural variations rather than deficits (Walker, 2021). Furthermore, it serves as a critical framework that reveals how systems are frequently designed according to neurotypical norms.

Within EdTech, this raises a fundamental tension:

While platforms claim personalisation, they are frequently built on standardised models of attention, motivation, and progression.

This analysis contends that EdTech systems are structurally neurotypical by design, and that sustainability depends on the degree to which these systems accommodate diverse cognitive experiences.

 

Conceptualising Sustainability

To address conceptual ambiguity, this paper defines sustainability across three interrelated dimensions:

1. Cognitive Sustainability
   The ability to engage in learning without excessive cognitive load, fatigue, or burnout (Sweller et al., 2020).
2. Social Sustainability
   The extent to which learning is supported by meaningful social interaction, belonging, and community (Wenger-Trayner & Wenger-Trayner, 2020).
3. Infrastructural Sustainability
   The stability, accessibility, and ethical governance of technological systems, including data practices (Williamson & Eynon, 2020).

EdTech and the Conditions of Possibility for Lifelong Learning

EdTech offers significant opportunities for lifelong learning, especially when its design aligns with the needs of neurodiverse learners.

Flexibility and Accessibility

Digital platforms enable asynchronous, location-independent learning, reducing barriers associated with traditional educational environments (Means & Neisler, 2021). For neurodiverse learners, this can mitigate challenges such as sensory overload and rigid scheduling (Al-Azawei et al., 2020).

Personalisation and AI

AI-driven systems promise adaptive learning pathways tailored to individual needs (Holmes et al., 2022). Emerging research suggests that such systems can support neurodiverse learners through:

• Adjustable pacing
• Alternative content formats
• Immediate feedback (Chen et al., 2024)

However, personalisation in these systems is often grounded in behavioural data rather than in-depth cognitive or emotional understanding, limiting its overall effectiveness.

Micro-Credentials and Modular Learning

Micro-credentials facilitate ongoing, flexible skill acquisition that aligns with labor market demands (Wheelahan & Moodie, 2021). Although they improve access, these credentials may fragment knowledge and prioritize employability at the expense of intellectual development.

Constraints on Sustainability in AI-Mediated EdTech

Cognitive Load, Motivation, and Burnout

Sustaining engagement in digital learning environments is difficult (Kizilcec et al., 2020; Zawacki-Richter et al., 2023). For neurodiverse learners, challenges include:

• Overstimulating interfaces
• Executive functioning demands
• Continuous self-regulation

Cognitive load theory indicates that inadequately designed environments can overwhelm learners and undermine sustained engagement (Sweller et al., 2020).

Inequality and Structural Exclusion

Despite claims of democratisation, EdTech often reproduces existing inequalities (Selwyn, 2022). Access to devices, connectivity, and digital literacy remains uneven, while inclusive design is inconsistently implemented.

For neurodiverse learners, these conditions result in compounded exclusion, as structural and cognitive barriers intersect.

AI, Datafication, and Governance

AI plays an increasingly central role in shaping learning experiences through:

• Predictive analytics
• Algorithmic recommendations
• Automated assessment

However, these systems raise critical concerns (Williamson & Eynon, 2020):

• Behavioural data is used to define “engagement”
• Neurodiverse patterns may be misinterpreted.
• Learners are subject to opaque decision-making processes.

This reflects a broader shift toward data-driven governance of learning, where sustainability is influenced by algorithmic systems beyond learner control.

Fragmentation and the Limits of Microlearning

Although modular learning enhances flexibility, it may also fragment knowledge (Fawns et al., 2023). As a result, lifelong learning risks devolving into a collection of disconnected competencies, which can undermine deeper understanding and critical thinking.

 Sustainable Lifelong Learning: Neurodiversity-Informed Approach

Designing for Cognitive Diversity

Sustainable EdTech must move beyond generic personalisation toward neurodiversity-informed design, including:

• Multimodal content
• Reduced sensory complexity
• Flexible pacing
• Predictable structures

Universal Design for Learning (CAST, 2021) provides a foundation but requires more rigorous implementation.

Socially Embedded Learning

Learning sustainability is enhanced when educational experiences are embedded within social contexts (Wenger-Trayner & Wenger-Trayner, 2020). Peer interaction, mentorship, and collaborative learning foster motivation and a sense of belonging.

For neurodiverse learners, controlled and flexible social engagement is particularly important.

Ethical AI and Data Practices

Sustainability requires transparent and inclusive data governance, including:

• User control over data
• Algorithmic accountability
• Recognition of diverse behavioural patterns

In the absence of these measures, EdTech is likely to reinforce existing inequities and erode trust among learners.

Reframing Lifelong Learning

This analysis advocates critical reframing:

Lifelong learning should not be understood solely as an economic imperative, but as a human-centred practice that accommodates diverse cognitive rhythms, including rest, discontinuity, and non-linear progression.

Discussion: Lifelong Learning as a Sociotechnical Regime

The findings indicate that lifelong learning through EdTech operates as a sociotechnical regime that shifts responsibility for adaptation onto individuals. Although it provides flexibility and access, it simultaneously:

• Normalises continuous productivity
• Externalises responsibility for employability
• Obscures structural inequalities

For neurodiverse learners, this redistribution is uneven, often requiring greater cognitive and emotional labour to sustain engagement.

Thus, sustainability is not a neutral outcome but a contested and negotiated condition, shaped by power, design, and lived experience.

Conclusion

EdTech has the potential to support lifelong learning, but this potential is not inherently realised. Sustainability depends on aligning technological systems with inclusive pedagogical practices, ethical governance, and the lived realities of diverse learners.

For neurodiverse learners, this necessitates a fundamental shift from standardised, efficiency-driven models toward flexible, human-centred approaches. Absent such transformation, lifelong learning may become a mechanism of exclusion and unsustainable demand rather than a pathway to empowerment.

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