Adaptive Learning and EdTech in Contemporary Learning Environments: A Sociotechnical and Critical Analysis
Abstract
Adaptive learning technologies have
become central to contemporary educational transformation, offering
personalised, data-driven instruction that dynamically responds to individual
learner needs. Within the broader educational technology (EdTech) ecosystem,
adaptive systems are frequently presented as solutions to persistent challenges
such as differentiation, engagement, and efficiency. Nevertheless, their
integration into learning environments introduces significant pedagogical,
ethical, and sociotechnical concerns. This article critically examines adaptive
learning using a multi-layered framework, analysing its effects on pedagogy,
temporality, spatiality, and professional practice. Drawing on recent
scholarship (2020–2025), the paper contends that adaptive learning constitutes
not only a technological innovation but also a restructuring of educational
logics, shaped by data infrastructures and algorithmic governance. Although
adaptive systems offer potential advantages, particularly for personalised and inclusive
learning, they may also reinforce behaviourist models, limit epistemic
diversity, and intensify surveillance. The article concludes by advocating for
a critical approach to adaptive learning that emphasises teacher agency,
ethical data practices, and the socio-cultural dimensions of learning.
Introduction
The rapid expansion of educational
technology (EdTech) has profoundly reconfigured the landscape of teaching and
learning. Among the most influential developments is adaptive learning, a form
of technology-enhanced instruction that uses algorithmic systems to tailor
educational experiences to individual learners in real time. Promoted as a
mechanism to improve efficiency, personalisation, and learning outcomes,
adaptive learning has gained traction across the K–12, higher education, and
corporate training sectors (Holmes et al., 2022).
Despite widespread adoption, adaptive
learning remains both conceptually and practically contested. Proponents
emphasise its ability to address learner variability and enhance engagement,
whereas critics contend that it reduces learning to quantifiable behaviours and
embeds surveillance within educational processes (Selwyn, 2021). This tension
reflects broader debates regarding the role of technology in education,
especially the extent to which digital systems reshape rather than merely
support pedagogical practice.
A sociotechnical perspective is
adopted to analyse adaptive learning within contemporary learning environments.
Rather than treating technology as neutral, this approach considers how
adaptive systems interact with institutional structures, pedagogical assumptions,
and power relations. The analysis is organised around four key dimensions:
pedagogical transformation, temporal restructuring, spatial reconfiguration,
and professional implications for teachers. Ethical concerns related to
datafication and algorithmic decision-making, particularly in relation to
equity and inclusion, are also foregrounded.
Conceptualising
Adaptive Learning
Adaptive learning refers to the use of
computational systems to modify instructional content, sequencing, and feedback
based on learner data. These systems rely on learning analytics, machine
learning algorithms, and continuous assessment to generate personalised
learning pathways (Ifenthaler & Yau, 2020).
At a technical level, adaptive systems
operate through three core processes:
- Data Collection – capturing
learner interactions, including responses, time-on-task, and navigation patterns.
- Analysis and
Modelling – using algorithms to infer learner knowledge, preferences, and progress.
- Adaptation – dynamically
adjusting content difficulty, sequencing, and feedback.
This process establishes a feedback
loop in which the system continuously refines its understanding of the learner.
Although frequently described as “personalised learning,” adaptivity is
constrained by the system's design and the parameters of available data
(Williamson, 2023).
Pedagogical
Transformation: From Standardisation to Personalisation
A significant impact of adaptive
learning is its challenge to standardised instructional models. Traditional
educational systems are organised around uniform curricula, fixed pacing, and
collective progression. Adaptive learning disrupts these assumptions by
enabling differentiated pathways tailored to individual learners.
This shift aligns with constructivist
theories of learning, which emphasise the active construction of knowledge.
Adaptive systems can support this by providing immediate feedback, scaffolding,
and opportunities for mastery-based progression (Pane et al., 2020). For
example, learners who struggle with a concept can receive additional practice,
while those who demonstrate mastery can advance more quickly.
However, the pedagogical implications
are complex. Many adaptive systems are grounded in behaviourist principles,
focusing on measurable outcomes such as correct responses and completion rates.
This raises concerns about the depth and quality of learning, particularly in
domains that require critical thinking, creativity, and collaboration (Luckin
et al., 2022).
Furthermore, the logic of
personalisation may obscure the social dimensions of learning. Education
encompasses not only individual cognitive processes but also relational and
cultural activities. By prioritising individual pathways, adaptive systems risk
marginalising collaborative learning experiences and reducing opportunities for
dialogue and shared meaning-making.
Temporal
Restructuring: The End of Linear Learning
Adaptive learning also transforms the
temporal structure of education. Traditional models are based on linear
progression, with learners moving through content at a predetermined pace. In
contrast, adaptive systems enable non-linear, asynchronous learning
trajectories.
This has several implications:
- Decoupling of
time and learning – progress is based on mastery rather than duration.
- Flexible pacing – learners can
accelerate or decelerate according to their needs.
- Continuous
assessment – evaluation is embedded within the learning process.
These changes challenge institutional
norms such as timetables, semesters, and standardised testing. Although
flexibility can enhance accessibility, it also raises questions regarding
coherence and accountability. For instance, assessment practices must be
reconsidered when each learner follows a unique pathway.
Additionally, the emphasis on
efficiency may result in prioritising speed over depth. Adaptive systems
frequently optimise rapid progression, which can discourage reflection and
exploration. This trend reflects broader concerns about the commodification of
education, where learning is treated as a process to be streamlined rather than
a complex, transformative experience (Biesta, 2021).
Spatial
Reconfiguration: Distributed Learning Environments
The integration of adaptive learning
technologies contributes to the spatial transformation of education. Learning
is no longer confined to physical classrooms but extends across digital
platforms, creating hybrid and distributed environments.
Key features of this shift include:
- Platform-mediated
learning – digital systems become central sites of educational activity.
- Blurring of
boundaries – distinctions between school, home, and informal learning contexts
become less clear.
- Increased
accessibility – learners can engage with content anytime and anywhere.
Although these developments expand
opportunities for participation, they also introduce new dependencies.
Educational institutions increasingly rely on proprietary platforms, raising
concerns regarding control, sustainability, and equity. Access to adaptive
learning is frequently mediated by infrastructure, such as devices,
connectivity, and institutional resources, which can exacerbate existing
inequalities (Selwyn, 2021).
Moreover, the spatial reconfiguration
of learning environments alters the nature of teacher–student interaction.
Digital platforms mediate communication, which may reduce the immediacy and
richness of face-to-face engagement. These changes have implications for
relationships, motivation, and the affective dimensions of learning.
Datafication and
Surveillance
Adaptive learning systems are
fundamentally data-driven. They rely on the continuous collection and analysis
of learner data to function effectively. This process, often referred to as
datafication, transforms educational activity into quantifiable metrics.
While data can provide valuable
insights, it also raises significant ethical concerns:
- Privacy and
consent – learners may not fully understand how their data is collected and
used.
- Algorithmic
bias – models may reproduce or amplify existing inequalities.
- Transparency –
decision-making processes are often opaque.
The concept of “surveillance pedagogy”
describes the monitoring practices embedded in EdTech systems (Williamson,
2023). In adaptive learning environments, every interaction may be recorded and
analysed, resulting in the creation of detailed learner profiles.
These practices have implications for
autonomy and agency. Learners may feel constrained by system-generated
recommendations, while teachers may experience pressure to align their practice
with algorithmic outputs. The authority of algorithms can challenge
professional judgement, resulting in a reconfiguration of power within
educational settings.
Implications for
Teachers: Reconfiguration of Professional Practice
Adaptive learning does not eliminate
the role of teachers but transforms it into significant ways. Rather than
serving primarily as content deliverers, teachers become facilitators,
interpreters, and designers of learning experiences.
Key changes include:
- Data
interpretation – teachers must make sense of system-generated insights.
- Targeted
intervention – identifying and supporting learners who require additional assistance.
- Pedagogical
mediation – balancing technological recommendations with professional expertise.
Although these shifts can enhance
instructional precision, they also introduce new challenges. Teachers may
experience reduced autonomy as decision-making becomes increasingly influenced
by algorithms. Furthermore, integrating adaptive systems often necessitates new
skills and competencies, such as data literacy and technological proficiency.
These developments intersect with
broader issues of teacher precarity, especially within globalised education
systems. Increased reliance on EdTech can result in the standardisation of
teaching practices and the outsourcing of pedagogical functions to technology
providers (Williamson, 2023). These trends prompt questions regarding the
future of the teaching profession and the nature of professional expertise.
Adaptive Learning and
Neurodiversity
Adaptive learning holds promise for
supporting neurodiverse learners. By tailoring instruction to individual needs,
adaptive systems can provide more inclusive and accessible learning
experiences.
Potential benefits include:
- Flexible pacing – reducing
cognitive overload.
- Customised
feedback – supporting different learning styles.
- Reduced stigma – allowing
learners to progress privately.
However, these benefits are not
assured. The effectiveness of adaptive learning for neurodiverse learners
depends on system design and the underlying assumptions. Many systems are based
on normative models of learning, which may not adequately accommodate diverse
cognitive profiles.
Furthermore, the emphasis on
individualisation may overlook the importance of social interaction and
collaborative learning, which are essential for many learners. A sole
technological approach to inclusion risks reducing complex educational needs to
technical problems.
Critical
Perspectives: The Limits of Adaptivity
Despite its potential, adaptive
learning is not a panacea. Several critical perspectives highlight its
limitations:
- The Myth of
Personalisation
Personalisation is often constrained by predefined pathways and limited datasets. True individualisation may be more rhetorical than real. - Epistemic
Narrowing
Adaptive systems prioritise what can be measured, potentially excluding forms of knowledge that are difficult to quantify. - Platform
Dependency
Institutions have become reliant on commercial technologies, raising concerns about control and long-term sustainability. - Reductionism
Learning is reduced to observable behaviours, neglecting affective, social, and cultural dimensions.
These critiques indicate that adaptive
learning should be understood within broader socio-economic and political
contexts. Adaptive learning is not merely a tool but part of a larger system
that shapes educational practice and policy.
Toward a Critical
Framework for Adaptive Learning
To address these challenges, a
critical approach to adaptive learning is needed. This involves:
- Reasserting
teacher agency – ensuring that technology supports rather than replaces
professional judgement.
- Promoting
ethical data practices – prioritising transparency, consent, and equity
- Emphasising
sociocultural learning – integrating collaborative and dialogic
approaches
- Interrogating
assumptions – questioning the values embedded in technological systems.
This framework aligns with emerging
scholarship in critical digital pedagogy, which seeks to balance technological
innovation with humanistic and democratic principles.
Conclusion
Adaptive learning constitutes a
significant development in the evolution of EdTech, providing new possibilities
for personalised and data-driven education. Its impact, however, extends beyond
technical functionality, reshaping pedagogical practices, institutional
structures, and power relations.
This article contends that adaptive
learning should be understood as a sociotechnical phenomenon, embedded within
broader systems of data, governance, and ideology. While adaptive learning has
the potential to enhance learning environments, it also presents risks related
to surveillance, reductionism, and inequality.
The challenge for educators,
researchers, and policymakers is not merely to adopt adaptive learning
technologies but to critically engage with their implications. By prioritising
ethical considerations, teacher agency, and the social dimensions of learning,
the benefits of adaptive learning can be harnessed while its limitations are
mitigated.
The central question concerns not
whether adaptive learning is effective, but what forms of education it enables
and for which learners.
References
Biesta, G. (2021). World-centred
education: A view for the present. Routledge.
Holmes, W., Bialik, M., & Fadel,
C. (2022). Artificial intelligence in education: Promises and implications
for teaching and learning. Center for Curriculum Redesign.
Ifenthaler, D., & Yau, J. Y. K.
(2020). Utilising learning analytics for study success: Reflections on current
empirical findings. Research and Practice in Technology Enhanced Learning,
15(1), 1–17.
Luckin, R., Holmes, W., Griffiths, M.,
& Forcier, L. B. (2022). Intelligence unleashed: An argument for AI in
education. Pearson.
Pane, J. F., Steiner, E. D., Baird, M.
D., & Hamilton, L. S. (2020). Continued progress: Promising evidence on
personalised learning. Educational Evaluation and Policy Analysis, 42(3),
346–365.
Selwyn, N. (2021). Education and
technology: Key issues and debates (3rd ed.). Bloomsbury.
Williamson, B. (2023). Big data in
education: The digital future of learning, policy and practice (2nd ed.).
Sage.
Adaptive Learning and
EdTech in Contemporary Learning Environments: A Sociotechnical and Critical
Analysis
Abstract
Adaptive learning technologies have
become central to contemporary educational transformation, offering
personalised, data-driven instruction that dynamically responds to individual
learner needs. Within the broader educational technology (EdTech) ecosystem,
adaptive systems are frequently presented as solutions to persistent challenges
such as differentiation, engagement, and efficiency. Nevertheless, their
integration into learning environments introduces significant pedagogical,
ethical, and sociotechnical concerns. This article critically examines adaptive
learning using a multi-layered framework, analysing its effects on pedagogy,
temporality, spatiality, and professional practice. Drawing on recent
scholarship (2020–2025), the paper contends that adaptive learning constitutes
not only a technological innovation but also a restructuring of educational
logics, shaped by data infrastructures and algorithmic governance. Although
adaptive systems offer potential advantages, particularly for personalised and inclusive
learning, they may also reinforce behaviourist models, limit epistemic
diversity, and intensify surveillance. The article concludes by advocating for
a critical approach to adaptive learning that emphasises teacher agency,
ethical data practices, and the socio-cultural dimensions of learning.
Introduction
The rapid expansion of educational
technology (EdTech) has profoundly reconfigured the landscape of teaching and
learning. Among the most influential developments is adaptive learning, a form
of technology-enhanced instruction that uses algorithmic systems to tailor
educational experiences to individual learners in real time. Promoted as a
mechanism to improve efficiency, personalisation, and learning outcomes,
adaptive learning has gained traction across the K–12, higher education, and
corporate training sectors (Holmes et al., 2022).
Despite widespread adoption, adaptive
learning remains both conceptually and practically contested. Proponents
emphasise its ability to address learner variability and enhance engagement,
whereas critics contend that it reduces learning to quantifiable behaviours and
embeds surveillance within educational processes (Selwyn, 2021). This tension
reflects broader debates regarding the role of technology in education,
especially the extent to which digital systems reshape rather than merely
support pedagogical practice.
A sociotechnical perspective is
adopted to analyse adaptive learning within contemporary learning environments.
Rather than treating technology as neutral, this approach considers how
adaptive systems interact with institutional structures, pedagogical assumptions,
and power relations. The analysis is organised around four key dimensions:
pedagogical transformation, temporal restructuring, spatial reconfiguration,
and professional implications for teachers. Ethical concerns related to
datafication and algorithmic decision-making, particularly in relation to
equity and inclusion, are also foregrounded.
Conceptualising
Adaptive Learning
Adaptive learning refers to the use of
computational systems to modify instructional content, sequencing, and feedback
based on learner data. These systems rely on learning analytics, machine
learning algorithms, and continuous assessment to generate personalised
learning pathways (Ifenthaler & Yau, 2020).
At a technical level, adaptive systems
operate through three core processes:
- Data Collection – capturing
learner interactions, including responses, time-on-task, and navigation patterns.
- Analysis and
Modelling – using algorithms to infer learner knowledge, preferences, and progress.
- Adaptation – dynamically
adjusting content difficulty, sequencing, and feedback.
This process establishes a feedback
loop in which the system continuously refines its understanding of the learner.
Although frequently described as “personalised learning,” adaptivity is
constrained by the system's design and the parameters of available data
(Williamson, 2023).
Pedagogical
Transformation: From Standardisation to Personalisation
A significant impact of adaptive
learning is its challenge to standardised instructional models. Traditional
educational systems are organised around uniform curricula, fixed pacing, and
collective progression. Adaptive learning disrupts these assumptions by
enabling differentiated pathways tailored to individual learners.
This shift aligns with constructivist
theories of learning, which emphasise the active construction of knowledge.
Adaptive systems can support this by providing immediate feedback, scaffolding,
and opportunities for mastery-based progression (Pane et al., 2020). For
example, learners who struggle with a concept can receive additional practice,
while those who demonstrate mastery can advance more quickly.
However, the pedagogical implications
are complex. Many adaptive systems are grounded in behaviourist principles,
focusing on measurable outcomes such as correct responses and completion rates.
This raises concerns about the depth and quality of learning, particularly in
domains that require critical thinking, creativity, and collaboration (Luckin
et al., 2022).
Furthermore, the logic of
personalisation may obscure the social dimensions of learning. Education
encompasses not only individual cognitive processes but also relational and
cultural activities. By prioritising individual pathways, adaptive systems risk
marginalising collaborative learning experiences and reducing opportunities for
dialogue and shared meaning-making.
Temporal
Restructuring: The End of Linear Learning
Adaptive learning also transforms the
temporal structure of education. Traditional models are based on linear
progression, with learners moving through content at a predetermined pace. In
contrast, adaptive systems enable non-linear, asynchronous learning
trajectories.
This has several implications:
- Decoupling of
time and learning – progress is based on mastery rather than duration.
- Flexible pacing – learners can
accelerate or decelerate according to their needs.
- Continuous
assessment – evaluation is embedded within the learning process.
These changes challenge institutional
norms such as timetables, semesters, and standardised testing. Although
flexibility can enhance accessibility, it also raises questions regarding
coherence and accountability. For instance, assessment practices must be
reconsidered when each learner follows a unique pathway.
Additionally, the emphasis on
efficiency may result in prioritising speed over depth. Adaptive systems
frequently optimise rapid progression, which can discourage reflection and
exploration. This trend reflects broader concerns about the commodification of
education, where learning is treated as a process to be streamlined rather than
a complex, transformative experience (Biesta, 2021).
Spatial
Reconfiguration: Distributed Learning Environments
The integration of adaptive learning
technologies contributes to the spatial transformation of education. Learning
is no longer confined to physical classrooms but extends across digital
platforms, creating hybrid and distributed environments.
Key features of this shift include:
- Platform-mediated
learning – digital systems become central sites of educational activity.
- Blurring of
boundaries – distinctions between school, home, and informal learning contexts
become less clear.
- Increased
accessibility – learners can engage with content anytime and anywhere.
Although these developments expand
opportunities for participation, they also introduce new dependencies.
Educational institutions increasingly rely on proprietary platforms, raising
concerns regarding control, sustainability, and equity. Access to adaptive
learning is frequently mediated by infrastructure, such as devices,
connectivity, and institutional resources, which can exacerbate existing
inequalities (Selwyn, 2021).
Moreover, the spatial reconfiguration
of learning environments alters the nature of teacher–student interaction.
Digital platforms mediate communication, which may reduce the immediacy and
richness of face-to-face engagement. These changes have implications for
relationships, motivation, and the affective dimensions of learning.
Datafication and
Surveillance
Adaptive learning systems are
fundamentally data-driven. They rely on the continuous collection and analysis
of learner data to function effectively. This process, often referred to as
datafication, transforms educational activity into quantifiable metrics.
While data can provide valuable
insights, it also raises significant ethical concerns:
- Privacy and
consent – learners may not fully understand how their data is collected and
used.
- Algorithmic
bias – models may reproduce or amplify existing inequalities.
- Transparency –
decision-making processes are often opaque.
The concept of “surveillance pedagogy”
describes the monitoring practices embedded in EdTech systems (Williamson,
2023). In adaptive learning environments, every interaction may be recorded and
analysed, resulting in the creation of detailed learner profiles.
These practices have implications for
autonomy and agency. Learners may feel constrained by system-generated
recommendations, while teachers may experience pressure to align their practice
with algorithmic outputs. The authority of algorithms can challenge
professional judgement, resulting in a reconfiguration of power within
educational settings.
Implications for
Teachers: Reconfiguration of Professional Practice
Adaptive learning does not eliminate
the role of teachers but transforms it into significant ways. Rather than
serving primarily as content deliverers, teachers become facilitators,
interpreters, and designers of learning experiences.
Key changes include:
- Data
interpretation – teachers must make sense of system-generated insights.
- Targeted
intervention – identifying and supporting learners who require additional assistance.
- Pedagogical
mediation – balancing technological recommendations with professional expertise.
Although these shifts can enhance
instructional precision, they also introduce new challenges. Teachers may
experience reduced autonomy as decision-making becomes increasingly influenced
by algorithms. Furthermore, integrating adaptive systems often necessitates new
skills and competencies, such as data literacy and technological proficiency.
These developments intersect with
broader issues of teacher precarity, especially within globalised education
systems. Increased reliance on EdTech can result in the standardisation of
teaching practices and the outsourcing of pedagogical functions to technology
providers (Williamson, 2023). These trends prompt questions regarding the
future of the teaching profession and the nature of professional expertise.
Adaptive Learning and
Neurodiversity
Adaptive learning holds promise for
supporting neurodiverse learners. By tailoring instruction to individual needs,
adaptive systems can provide more inclusive and accessible learning
experiences.
Potential benefits include:
- Flexible pacing – reducing
cognitive overload.
- Customised
feedback – supporting different learning styles.
- Reduced stigma – allowing
learners to progress privately.
However, these benefits are not
assured. The effectiveness of adaptive learning for neurodiverse learners
depends on system design and the underlying assumptions. Many systems are based
on normative models of learning, which may not adequately accommodate diverse
cognitive profiles.
Furthermore, the emphasis on
individualisation may overlook the importance of social interaction and
collaborative learning, which are essential for many learners. A sole
technological approach to inclusion risks reducing complex educational needs to
technical problems.
Critical
Perspectives: The Limits of Adaptivity
Despite its potential, adaptive
learning is not a panacea. Several critical perspectives highlight its
limitations:
- The Myth of
Personalisation
Personalisation is often constrained by predefined pathways and limited datasets. True individualisation may be more rhetorical than real. - Epistemic
Narrowing
Adaptive systems prioritise what can be measured, potentially excluding forms of knowledge that are difficult to quantify. - Platform
Dependency
Institutions have become reliant on commercial technologies, raising concerns about control and long-term sustainability. - Reductionism
Learning is reduced to observable behaviours, neglecting affective, social, and cultural dimensions.
These critiques indicate that adaptive
learning should be understood within broader socio-economic and political
contexts. Adaptive learning is not merely a tool but part of a larger system
that shapes educational practice and policy.
Toward a Critical
Framework for Adaptive Learning
To address these challenges, a
critical approach to adaptive learning is needed. This involves:
- Reasserting
teacher agency – ensuring that technology supports rather than replaces
professional judgement.
- Promoting
ethical data practices – prioritising transparency, consent, and equity
- Emphasising
sociocultural learning – integrating collaborative and dialogic
approaches
- Interrogating
assumptions – questioning the values embedded in technological systems.
This framework aligns with emerging
scholarship in critical digital pedagogy, which seeks to balance technological
innovation with humanistic and democratic principles.
Conclusion
Adaptive learning constitutes a
significant development in the evolution of EdTech, providing new possibilities
for personalised and data-driven education. Its impact, however, extends beyond
technical functionality, reshaping pedagogical practices, institutional
structures, and power relations.
This article contends that adaptive
learning should be understood as a sociotechnical phenomenon, embedded within
broader systems of data, governance, and ideology. While adaptive learning has
the potential to enhance learning environments, it also presents risks related
to surveillance, reductionism, and inequality.
The challenge for educators,
researchers, and policymakers is not merely to adopt adaptive learning
technologies but to critically engage with their implications. By prioritising
ethical considerations, teacher agency, and the social dimensions of learning,
the benefits of adaptive learning can be harnessed while its limitations are
mitigated.
The central question concerns not
whether adaptive learning is effective, but what forms of education it enables
and for which learners.
References
Biesta, G. (2021). World-centred
education: A view for the present. Routledge.
Holmes, W., Bialik, M., & Fadel,
C. (2022). Artificial intelligence in education: Promises and implications
for teaching and learning. Center for Curriculum Redesign.
Ifenthaler, D., & Yau, J. Y. K.
(2020). Utilising learning analytics for study success: Reflections on current
empirical findings. Research and Practice in Technology Enhanced Learning,
15(1), 1–17.
Luckin, R., Holmes, W., Griffiths, M.,
& Forcier, L. B. (2022). Intelligence unleashed: An argument for AI in
education. Pearson.
Pane, J. F., Steiner, E. D., Baird, M.
D., & Hamilton, L. S. (2020). Continued progress: Promising evidence on
personalised learning. Educational Evaluation and Policy Analysis, 42(3),
346–365.
Selwyn, N. (2021). Education and
technology: Key issues and debates (3rd ed.). Bloomsbury.
Williamson, B. (2023). Big data in
education: The digital future of learning, policy and practice (2nd ed.).
Sage.
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