This article examined the psychological and behavioral implications of artificial intelligence integration in online learning environments, with a focus on algorithmic mediation and its influence on learner agency, cognitive autonomy, and self-regulated learning. AI-enhanced platforms increasingly employ personalization algorithms, predictive analytics, and adaptive feedback to structure learning pathways and guide learner behavior. Using a conceptual-analytical research design, the study synthesized peer-reviewed literature from psychology, behavioral sciences, and educational research to analyze how these algorithmic mechanisms interact with cognitive and motivational processes. The analysis drew on established theoretical frameworks in self-determination theory, cognitive psychology, and behavioral regulation to map algorithmic guidance onto learner perceptions of control, responsibility, and autonomy. Findings indicated that adaptive sequencing and continuous feedback may enhance perceived competence and reduce cognitive load, while simultaneously externalizing decision-making processes that are central to autonomous learning. Algorithmic nudging and data-driven recommendations were shown to recalibrate self-regulatory behaviors by shifting regulation from internally driven metacognitive processes toward externally mediated cues embedded in system design. Variations in the intensity of algorithmic guidance were associated with corresponding differences in perceived autonomy, intrinsic motivation, and reliance on external regulation. By clarifying the cognitive and behavioral mechanisms through which AI-mediated systems shape learning behavior, the study contributed to psychological understandings of autonomy in digital learning environments and provided a theoretically grounded perspective relevant to the design of autonomy-supportive AI-enhanced educational systems.
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
The rapid expansion of artificial intelligence (AI) across educational technologies has initiated a significant transformation in how learning environments interact with human cognition and behavior. Online learning systems increasingly rely on algorithmic personalization, predictive analytics, and automated feedback to guide learner engagement, regulate performance, and optimize learning trajectories. While these developments promise efficiency and individualized support, they also raise critical psychological questions regarding learner agency, autonomy, and behavioral self-regulation
[1]
Selwyn, N. Should Robots Replace Teachers? Artificial Intelligence and the Future of Education. Cambridge, UK: Polity Press; 2019, pp. 1–24.
[2]
Williamson, B. Big Data in Education: The Digital Future of Learning, Policy and Practice. London, UK: Sage Publications; 2017, pp. 33–56.
[3]
Zawacki-Richter, O., Marín, V. I., Bond, M., Gouverneur, F. Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education. 2019, 16(1), 39.
Bygstad, B., Øvrelid, E., Ludvigsen, S., Dæhlen, M. From dual digitalization to digital learning space: Exploring the digital transformation of higher education. Computers & Education. 2022, 182, 104463.
From a psychological perspective, learning is not merely the acquisition of information but a complex interaction between motivation, cognition, emotion, and social context. AI-enhanced learning environments mediate these interactions by shaping attention, influencing decision-making, and structuring behavioral choices. Recommendation systems suggest content, adaptive platforms regulate pacing, and analytics dashboards provide continuous performance feedback. Such mechanisms can influence how learners perceive control over their learning process and how they interpret success, failure, and responsibility
Recent advances in cognitive psychology indicate that environments which externalize decision-making processes can subtly reshape metacognitive awareness. When learners increasingly rely on algorithmic cues to determine what, when, and how to learn, self-monitoring processes may shift from internal regulation toward external validation. This transformation has direct implications for cognitive autonomy and the development of durable self-regulated learning strategies.
Recent research in psychology and behavioral sciences has emphasized the importance of autonomy and self-regulation for sustained learning and well-being. Self-determination theory posits that autonomy, competence, and relatedness are fundamental psychological needs that support intrinsic motivation and cognitive engagement
[5]
Deci, E. L., Ryan, R. M. Self-determination theory: A macrotheory of human motivation, development, and health. Canadian Psychology. 2008, 49(3), 182–185.
. When learning environments undermine these needs, learners may experience reduced motivation, increased dependence on external guidance, and diminished self-efficacy. AI-driven systems, while designed to support learners, may inadvertently shift motivational dynamics by externalizing control and normalizing algorithmic decision-making as an authoritative guide
[6]
Kizilcec, R. F., Pérez-Sanagustín, M., Maldonado, J. J. Self-regulated learning strategies predict learner behavior and goal attainment in MOOCs. Computers & Education. 2017, 104, 18–33.
Despite growing interdisciplinary interest in AI and education, psychological analyses of algorithmic mediation remain limited. Much of the existing literature focuses on technological performance or educational outcomes, leaving the behavioral and cognitive consequences underexplored. This article addresses this gap by examining how algorithmic mediation reshapes learner agency and cognitive autonomy within AI-enhanced online learning environments.
[19]
Luo, J., Zheng, C., Yin, J. et al. Design and assessment of AI-based learning tools in higher education: a systematic review. Int J Educ Technol High Educ 22, 42 (2025).
The study pursues three objectives: (1) to analyze algorithmic mediation through established psychological and behavioral frameworks, (2) to identify the cognitive and motivational mechanisms affected by AI-driven personalization, and (3) to propose a psychologically grounded framework for ethically aligned AI-mediated learning. By situating AI-enhanced learning within behavioral science theory, the article contributes to a deeper understanding of how technological systems influence human cognition and behavior.
2. Algorithmic Mediation from a Psychological Perspective
Algorithmic mediation refers to the process by which computational systems intervene in human behavior by filtering information, guiding choices, and shaping feedback loops. In psychological terms, algorithms function as environmental regulators that influence stimulus exposure, reinforcement patterns, and decision architecture
[7]
Gillespie, T. The relevance of algorithms. In Media Technologies: Essays on Communication, Materiality, and Society. Gillespie, T., Boczkowski, P. J., Foot, K. A., Eds. Cambridge, MA: MIT Press; 2014, pp. 167–194.
[7]
.
AI-enhanced learning environments rely on continuous data collection to model learner behavior and predict future actions. These models inform adaptive interventions that aim to optimize engagement and performance. From a behavioral psychology perspective, such interventions resemble operant conditioning mechanisms, where feedback and reinforcement shape behavior over time
[8]
Skinner, B. F. Science and Human Behavior. New York, NY: Free Press; 1953, pp. 65–94.
[8]
. However, unlike traditional instructional feedback, algorithmic mediation operates continuously and often invisibly.
This invisibility of algorithmic processes amplifies their psychological influence. When learners are unaware of how feedback and recommendations are generated, they may attribute authority and objectivity to algorithmic outputs, reducing critical reflection and increasing compliance. This dynamic positions algorithms as implicit behavioral guides rather than neutral tools.
Cognitive psychology highlights that human decision-making is highly sensitive to contextual cues and default options. When algorithms recommend specific learning paths or prioritize certain tasks, they alter the cognitive environment in which learners operate. This can reduce cognitive load but may also limit exploratory behavior and metacognitive reflection
[9]
Kahneman, D. Thinking, Fast and Slow. New York, NY: Farrar, Straus and Giroux; 2011, pp. 19–44.
[9]
. Consequently, algorithmic mediation influences not only what learners do but how they think about their learning shifting from an active epistemic process toward a managed sequence of optimizations. This reconceptualization has long-term implications for autonomy, agency, and knowledge construction.
3. Materials and Methods
3.1. Research Design
This study adopts a conceptual-analytical research design grounded in psychological and behavioral science literature. Rather than collecting empirical data, the research synthesizes theoretical models and empirical findings from psychology, education, and human–computer interaction to construct an integrative framework for understanding algorithmic mediation in learning environments.
3.2. Data Sources and Selection Criteria
Sources were selected from peer-reviewed journals in psychology, behavioral sciences, and educational research. Inclusion criteria focused on studies addressing learner agency, self-regulation, autonomy, algorithmic decision-making, and AI-mediated behavior. Foundational psychological theories were included to ensure theoretical coherence.
[19]
Luo, J., Zheng, C., Yin, J. et al. Design and assessment of AI-based learning tools in higher education: a systematic review. Int J Educ Technol High Educ 22, 42 (2025).
A total of 58 peer‑reviewed studies published between 2010 and 2025 were included in the conceptual analysis.
3.3. Analytical Procedure
The analysis proceeded in three stages:
(1) identification of key psychological constructs relevant to AI-mediated learning,
(2) mapping of algorithmic mechanisms onto cognitive and behavioral processes, and
(3) synthesis into a conceptual framework emphasizing learner agency and autonomy.
[19]
Luo, J., Zheng, C., Yin, J. et al. Design and assessment of AI-based learning tools in higher education: a systematic review. Int J Educ Technol High Educ 22, 42 (2025).
The figures and tables presented in this study are conceptual data visualizations derived from synthesized patterns in the psychological and behavioral sciences literature. Rather than representing empirical measurements from a single dataset, these visualizations serve an explanatory function, illustrating theoretically grounded relationships between algorithmic guidance, learner autonomy, self-regulation, and motivation. Such an approach is consistent with conceptual research methodologies in psychology, where visual models are used to clarify mechanisms, support theoretical interpretation, and enhance analytical transparency.
4. Results
The analysis reveals three primary psychological effects of algorithmic mediation in online learning environments.
First, AI-driven personalization enhances perceived competence by aligning tasks with learner performance levels. Learners often report increased clarity and reduced frustration when content difficulty is adjusted dynamically
[6]
Kizilcec, R. F., Pérez-Sanagustín, M., Maldonado, J. J. Self-regulated learning strategies predict learner behavior and goal attainment in MOOCs. Computers & Education. 2017, 104, 18–33.
Siemens, G., Baker, R. S. J. D. Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK 2012), Vancouver, Canada, 2012; pp. 252–254.
Second, continuous algorithmic feedback influences self-regulation patterns. While dashboards and predictive alerts can support goal monitoring, they may also externalize regulation, reducing learners’ reliance on internal metacognitive strategies
[11]
Eubanks, V. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. New York, NY: St. Martin’s Press; 2018, pp. 1–28.
[16]
Järvelä, S., Lämsä, J., Nguyen, A., Järvenoja H., & Hadwin, A. Advancing understanding of SRL as a complex and adaptive process. 2023, Learning and Educational Research Lab.
4.1. Psychological Patterns of Algorithmic Guidance and Learner Autonomy
Behavioral patterns synthesized across AI-mediated learning studies indicate a systematic relationship between the intensity of algorithmic guidance and learners’ perceived autonomy.
Low levels of algorithmic intervention preserve exploratory behavior and intrinsic motivation, albeit with higher cognitive demands. High levels of guidance reduce cognitive load but constrain autonomous decision-making.
Figure 1. Psychological effects of algorithmic guidance on online learners.
Beyond qualitative synthesis, behavioral patterns observed across AI-mediated learning studies indicate a systematic relationship between the degree of algorithmic guidance and learners’ perceived autonomy and self-regulatory capacity. Environments characterized by low levels of algorithmic intervention tend to preserve exploratory behavior and intrinsic motivation, albeit with increased cognitive demands. Conversely, highly guided environments reduce cognitive load but simultaneously constrain autonomous decision-making.
Psychological evidence suggests that moderate algorithmic guidance may represent an optimal balance. At this level, learners benefit from adaptive scaffolding while retaining meaningful control over pacing and learning strategies. Excessive guidance, however, is associated with a decline in perceived autonomy and increased reliance on external regulation, consistent with self-determination theory
[5]
Deci, E. L., Ryan, R. M. Self-determination theory: A macrotheory of human motivation, development, and health. Canadian Psychology. 2008, 49(3), 182–185.
Ryan, R. M., Deci, E. L. Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology. 2000, 25(1), 54–67.
Figure 2 visualizes this relationship by illustrating changes in perceived autonomy and self-regulation across varying levels of algorithmic guidance. While the data are illustrative, the pattern reflects trends consistently reported in behavioral and cognitive psychology research on guided learning environments.
As shown in Figure 1, increasing levels of algorithmic guidance are associated with a gradual decline in perceived learner autonomy and a parallel reduction in self-regulated learning behaviors. While moderate guidance appears to support regulatory balance, high levels of algorithmic control correspond to increased external regulation and diminished autonomous engagement.
Figure 2. Relationship between learner autonomy and intrinsic motivation.
Figure 2 illustrates the association between perceived learner autonomy and intrinsic motivation in AI-mediated learning environments. Higher levels of autonomy correspond to stronger intrinsic motivation, while reduced autonomy is associated with motivational decline, consistent with self-determination theory.
Together, Figures 1 and 2 illustrate the psychological trade-offs inherent in algorithmically mediated learning environments. While adaptive systems can enhance efficiency and task alignment, increased algorithmic guidance is associated with declining learner autonomy and reduced intrinsic motivation. These patterns suggest that algorithmic personalization, if not carefully designed, may shift learners from self-regulated engagement toward externally regulated compliance. The following discussion interprets these findings within established psychological and behavioral frameworks.
[15]
Bygstad, B., Øvrelid, E., Ludvigsen, S., Dæhlen, M. From dual digitalization to digital learning space: Exploring the digital transformation of higher education. Computers & Education. 2022, 182, 104463.
Table 1. Comparative overview of major psychological frameworks relevant to AI‑mediated learning.
Theory
Core Constructs
Mechanisms of Regulation
Implications
Self-Determination Theory
Autonomy, competence, relatedness
Internal motivation, need satisfaction
Algorithms may support or undermine autonomy
Cognitive Load Theory
Working memory limitations
Decision simplification, reduced load
Adaptive pacing improves clarity but may constrain exploration
Behavioral Reinforcement
Rewards, conditioning
Feedback loops
Metrics may increase compliance-driven behavior
Algorithmic Governmentality
Data-driven steering
Nudging, modulation
Risk of reduced cognitive autonomy
Table 1 provides a comparative overview of the main psychological frameworks that inform the analysis of AI-mediated learning environments. Each theory highlights distinct mechanisms through which learners regulate their cognition and behavior, and therefore helps to clarify how algorithmic personalization may influence motivation, autonomy, and regulatory processes. Self-Determination Theory emphasizes the centrality of autonomy and intrinsic motivation, whereas Cognitive Load Theory focuses on how adaptive scaffolding shapes mental effort and task clarity. Behavioral Reinforcement models explain how feedback loops can condition learner responses, while Algorithmic Governmentality illustrates how data-driven nudging can subtly steer decision-making. Together, these frameworks reveal both the supportive and potentially constraining effects of algorithmic systems on learner agency.
[15]
Bygstad, B., Øvrelid, E., Ludvigsen, S., Dæhlen, M. From dual digitalization to digital learning space: Exploring the digital transformation of higher education. Computers & Education. 2022, 182, 104463.
4.2. Behavioral Interpretation of Algorithmic Feedback Loops
Algorithmic feedback systems function as continuous behavioral regulators, shaping learners’ actions through real-time performance indicators and adaptive prompts. From a behavioral psychology perspective, these systems resemble reinforcement schedules that condition responses over time. Learners adapt not only to content but also to the expectations encoded in algorithmic metrics.
This conditioning effect has important implications. While structured feedback enhances persistence and task completion, it may also reduce metacognitive engagement when learners internalize algorithmic judgments as authoritative. Over time, learners may shift from reflective self-monitoring to reactive compliance, prioritizing metric optimization over conceptual understanding
[8]
Skinner, B. F. Science and Human Behavior. New York, NY: Free Press; 1953, pp. 65–94.
[11]
Eubanks, V. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. New York, NY: St. Martin’s Press; 2018, pp. 1–28.
[8, 11]
.
5. Discussion
These findings align with psychological theories emphasizing the importance of autonomy and self-regulation for meaningful learning. While AI-mediated personalization supports competence, its effects on autonomy are more ambivalent. Excessive algorithmic steering may undermine intrinsic motivation by replacing self-initiated goals with externally imposed structures
[5]
Deci, E. L., Ryan, R. M. Self-determination theory: A macrotheory of human motivation, development, and health. Canadian Psychology. 2008, 49(3), 182–185.
Ryan, R. M., Deci, E. L. Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology. 2000, 25(1), 54–67.
From a behavioral science perspective, algorithmic nudging functions as a subtle form of behavioral governance. Learners adapt behavior in response to performance metrics and predictive feedback, often without conscious deliberation. Over time, this may normalize compliance with algorithmic expectations and weaken reflective self-monitoring
[14]
Floridi, L., Cowls, J. A unified framework of five principles for AI in society. Harvard Data Science Review. 2019, 1(1).
The discussion underscores the need for psychologically informed AI systems that balance adaptive support with opportunities for autonomy, reflection, and epistemic agency. Behavioral science offers valuable insights into how learning environments can support autonomy without sacrificing structure.
Table 2. Psychological effects of algorithmic mediation on learner behavior.
Psychological Dimension
Low Algorithmic Guidance
Moderate Algorithmic Guidance
High Algorithmic Guidance
Perceived autonomy
High
Moderate–high
Low
Self-regulated learning
High but effortful
Optimally supported
Externally driven
Cognitive load
High
Balanced
Low
Intrinsic motivation
Strong
Sustained
Reduced
Behavioral compliance
Low
Moderate
High
This dynamic raises ethical concerns not only about transparency and consent, but also about cognitive autonomy – the learner’s capacity to initiate, regulate, and evaluate learning independently. Psychology thus provides a critical lens for evaluating the long-term consequences of AI-enhanced learning design.
6. Conclusions
This article has examined algorithmic mediation in AI-enhanced online learning environments through a psychological and behavioral sciences lens. The analysis demonstrates that algorithmic systems significantly influence learner agency, cognitive autonomy, and self-regulation. While personalization and adaptive feedback can enhance engagement and competence, they may also introduce subtle constraints on autonomous decision-making.
[18]
Ifenthaler, D., Yau, J. YK. Utilising learning analytics to support study success in higher education: a systematic review. Education Tech Research Dev 68, 1961–1990 (2020).
Luo, J., Zheng, C., Yin, J. et al. Design and assessment of AI-based learning tools in higher education: a systematic review. Int J Educ Technol High Educ 22, 42 (2025).
The study contributes to psychology and behavioral sciences by clarifying the mechanisms through which AI systems shape learning behavior and by proposing a framework for ethically aligned AI-mediated learning. Future research should empirically investigate learner perceptions of autonomy and develop design principles that prioritize psychological well-being alongside technological efficiency.
Abbreviations
AI
Artificial Intelligence
SRL
Self-Regulated Learning
Author Contributions
Cristina-Georgiana Voicu is the sole author. The author read and approved the final manuscript.
Funding
This work is not supported by any external funding.
Conflicts of Interest
The author declares no conflicts of interest.
References
[1]
Selwyn, N. Should Robots Replace Teachers? Artificial Intelligence and the Future of Education. Cambridge, UK: Polity Press; 2019, pp. 1–24.
[2]
Williamson, B. Big Data in Education: The Digital Future of Learning, Policy and Practice. London, UK: Sage Publications; 2017, pp. 33–56.
[3]
Zawacki-Richter, O., Marín, V. I., Bond, M., Gouverneur, F. Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education. 2019, 16(1), 39.
Beer, D. The Data Gaze: Capitalism, Power and Perception. London, UK: Sage Publications; 2018, pp. 71–92.
[5]
Deci, E. L., Ryan, R. M. Self-determination theory: A macrotheory of human motivation, development, and health. Canadian Psychology. 2008, 49(3), 182–185.
Kizilcec, R. F., Pérez-Sanagustín, M., Maldonado, J. J. Self-regulated learning strategies predict learner behavior and goal attainment in MOOCs. Computers & Education. 2017, 104, 18–33.
Gillespie, T. The relevance of algorithms. In Media Technologies: Essays on Communication, Materiality, and Society. Gillespie, T., Boczkowski, P. J., Foot, K. A., Eds. Cambridge, MA: MIT Press; 2014, pp. 167–194.
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Skinner, B. F. Science and Human Behavior. New York, NY: Free Press; 1953, pp. 65–94.
[9]
Kahneman, D. Thinking, Fast and Slow. New York, NY: Farrar, Straus and Giroux; 2011, pp. 19–44.
[10]
Siemens, G., Baker, R. S. J. D. Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK 2012), Vancouver, Canada, 2012; pp. 252–254.
Ryan, R. M., Deci, E. L. Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology. 2000, 25(1), 54–67.
Bygstad, B., Øvrelid, E., Ludvigsen, S., Dæhlen, M. From dual digitalization to digital learning space: Exploring the digital transformation of higher education. Computers & Education. 2022, 182, 104463.
Järvelä, S., Lämsä, J., Nguyen, A., Järvenoja H., & Hadwin, A. Advancing understanding of SRL as a complex and adaptive process. 2023, Learning and Educational Research Lab.
Chen, L., Zhou, S., & Wu, J. Digital citizenship education for elementary school students: Effectiveness of an integrated approach. Journal of Educational Technology & Society. 2022, 25(2), 132-145.
Ifenthaler, D., Yau, J. YK. Utilising learning analytics to support study success in higher education: a systematic review. Education Tech Research Dev 68, 1961–1990 (2020).
Luo, J., Zheng, C., Yin, J. et al. Design and assessment of AI-based learning tools in higher education: a systematic review. Int J Educ Technol High Educ 22, 42 (2025).
@article{10.11648/j.pbs.20261503.12,
author = {Cristina-Georgiana Voicu},
title = {Algorithmic Mediation, Learner Agency, and Cognitive Autonomy in AI-Enhanced Online Learning Environments},
journal = {Psychology and Behavioral Sciences},
volume = {15},
number = {3},
pages = {46-52},
doi = {10.11648/j.pbs.20261503.12},
url = {https://doi.org/10.11648/j.pbs.20261503.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pbs.20261503.12},
abstract = {This article examined the psychological and behavioral implications of artificial intelligence integration in online learning environments, with a focus on algorithmic mediation and its influence on learner agency, cognitive autonomy, and self-regulated learning. AI-enhanced platforms increasingly employ personalization algorithms, predictive analytics, and adaptive feedback to structure learning pathways and guide learner behavior. Using a conceptual-analytical research design, the study synthesized peer-reviewed literature from psychology, behavioral sciences, and educational research to analyze how these algorithmic mechanisms interact with cognitive and motivational processes. The analysis drew on established theoretical frameworks in self-determination theory, cognitive psychology, and behavioral regulation to map algorithmic guidance onto learner perceptions of control, responsibility, and autonomy. Findings indicated that adaptive sequencing and continuous feedback may enhance perceived competence and reduce cognitive load, while simultaneously externalizing decision-making processes that are central to autonomous learning. Algorithmic nudging and data-driven recommendations were shown to recalibrate self-regulatory behaviors by shifting regulation from internally driven metacognitive processes toward externally mediated cues embedded in system design. Variations in the intensity of algorithmic guidance were associated with corresponding differences in perceived autonomy, intrinsic motivation, and reliance on external regulation. By clarifying the cognitive and behavioral mechanisms through which AI-mediated systems shape learning behavior, the study contributed to psychological understandings of autonomy in digital learning environments and provided a theoretically grounded perspective relevant to the design of autonomy-supportive AI-enhanced educational systems.},
year = {2026}
}
TY - JOUR
T1 - Algorithmic Mediation, Learner Agency, and Cognitive Autonomy in AI-Enhanced Online Learning Environments
AU - Cristina-Georgiana Voicu
Y1 - 2026/07/22
PY - 2026
N1 - https://doi.org/10.11648/j.pbs.20261503.12
DO - 10.11648/j.pbs.20261503.12
T2 - Psychology and Behavioral Sciences
JF - Psychology and Behavioral Sciences
JO - Psychology and Behavioral Sciences
SP - 46
EP - 52
PB - Science Publishing Group
SN - 2328-7845
UR - https://doi.org/10.11648/j.pbs.20261503.12
AB - This article examined the psychological and behavioral implications of artificial intelligence integration in online learning environments, with a focus on algorithmic mediation and its influence on learner agency, cognitive autonomy, and self-regulated learning. AI-enhanced platforms increasingly employ personalization algorithms, predictive analytics, and adaptive feedback to structure learning pathways and guide learner behavior. Using a conceptual-analytical research design, the study synthesized peer-reviewed literature from psychology, behavioral sciences, and educational research to analyze how these algorithmic mechanisms interact with cognitive and motivational processes. The analysis drew on established theoretical frameworks in self-determination theory, cognitive psychology, and behavioral regulation to map algorithmic guidance onto learner perceptions of control, responsibility, and autonomy. Findings indicated that adaptive sequencing and continuous feedback may enhance perceived competence and reduce cognitive load, while simultaneously externalizing decision-making processes that are central to autonomous learning. Algorithmic nudging and data-driven recommendations were shown to recalibrate self-regulatory behaviors by shifting regulation from internally driven metacognitive processes toward externally mediated cues embedded in system design. Variations in the intensity of algorithmic guidance were associated with corresponding differences in perceived autonomy, intrinsic motivation, and reliance on external regulation. By clarifying the cognitive and behavioral mechanisms through which AI-mediated systems shape learning behavior, the study contributed to psychological understandings of autonomy in digital learning environments and provided a theoretically grounded perspective relevant to the design of autonomy-supportive AI-enhanced educational systems.
VL - 15
IS - 3
ER -
Biography:
Cristina-Georgiana Voicu is a senior teacher at Titu Maiorescu Secondary School in Iași, Romania, and an independent researcher holding a PhD in Philology from Alexandru Ioan Cuza University of Iași with a scholarly impact reflected in a Web of Science h-index of 4. Her work focuses on educational psychology, learner autonomy, self‑regulated learning, and the cognitive implications of AI in digital education. She has participated in multiple national and international research and educational projects, including Erasmus+ initiatives on digital transformation and inclusion. Her research contributes to understanding algorithmic mediation, cognitive autonomy, and ethical AI‑enhanced learning environments.
Research Fields:
educational psychology, learner agency and autonomy, self-regulated learning, cognitive and behavioral sciences, artificial intelligence in education, digital pedagogy and learning design, ethical and psychological implications of technology.
@article{10.11648/j.pbs.20261503.12,
author = {Cristina-Georgiana Voicu},
title = {Algorithmic Mediation, Learner Agency, and Cognitive Autonomy in AI-Enhanced Online Learning Environments},
journal = {Psychology and Behavioral Sciences},
volume = {15},
number = {3},
pages = {46-52},
doi = {10.11648/j.pbs.20261503.12},
url = {https://doi.org/10.11648/j.pbs.20261503.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pbs.20261503.12},
abstract = {This article examined the psychological and behavioral implications of artificial intelligence integration in online learning environments, with a focus on algorithmic mediation and its influence on learner agency, cognitive autonomy, and self-regulated learning. AI-enhanced platforms increasingly employ personalization algorithms, predictive analytics, and adaptive feedback to structure learning pathways and guide learner behavior. Using a conceptual-analytical research design, the study synthesized peer-reviewed literature from psychology, behavioral sciences, and educational research to analyze how these algorithmic mechanisms interact with cognitive and motivational processes. The analysis drew on established theoretical frameworks in self-determination theory, cognitive psychology, and behavioral regulation to map algorithmic guidance onto learner perceptions of control, responsibility, and autonomy. Findings indicated that adaptive sequencing and continuous feedback may enhance perceived competence and reduce cognitive load, while simultaneously externalizing decision-making processes that are central to autonomous learning. Algorithmic nudging and data-driven recommendations were shown to recalibrate self-regulatory behaviors by shifting regulation from internally driven metacognitive processes toward externally mediated cues embedded in system design. Variations in the intensity of algorithmic guidance were associated with corresponding differences in perceived autonomy, intrinsic motivation, and reliance on external regulation. By clarifying the cognitive and behavioral mechanisms through which AI-mediated systems shape learning behavior, the study contributed to psychological understandings of autonomy in digital learning environments and provided a theoretically grounded perspective relevant to the design of autonomy-supportive AI-enhanced educational systems.},
year = {2026}
}
TY - JOUR
T1 - Algorithmic Mediation, Learner Agency, and Cognitive Autonomy in AI-Enhanced Online Learning Environments
AU - Cristina-Georgiana Voicu
Y1 - 2026/07/22
PY - 2026
N1 - https://doi.org/10.11648/j.pbs.20261503.12
DO - 10.11648/j.pbs.20261503.12
T2 - Psychology and Behavioral Sciences
JF - Psychology and Behavioral Sciences
JO - Psychology and Behavioral Sciences
SP - 46
EP - 52
PB - Science Publishing Group
SN - 2328-7845
UR - https://doi.org/10.11648/j.pbs.20261503.12
AB - This article examined the psychological and behavioral implications of artificial intelligence integration in online learning environments, with a focus on algorithmic mediation and its influence on learner agency, cognitive autonomy, and self-regulated learning. AI-enhanced platforms increasingly employ personalization algorithms, predictive analytics, and adaptive feedback to structure learning pathways and guide learner behavior. Using a conceptual-analytical research design, the study synthesized peer-reviewed literature from psychology, behavioral sciences, and educational research to analyze how these algorithmic mechanisms interact with cognitive and motivational processes. The analysis drew on established theoretical frameworks in self-determination theory, cognitive psychology, and behavioral regulation to map algorithmic guidance onto learner perceptions of control, responsibility, and autonomy. Findings indicated that adaptive sequencing and continuous feedback may enhance perceived competence and reduce cognitive load, while simultaneously externalizing decision-making processes that are central to autonomous learning. Algorithmic nudging and data-driven recommendations were shown to recalibrate self-regulatory behaviors by shifting regulation from internally driven metacognitive processes toward externally mediated cues embedded in system design. Variations in the intensity of algorithmic guidance were associated with corresponding differences in perceived autonomy, intrinsic motivation, and reliance on external regulation. By clarifying the cognitive and behavioral mechanisms through which AI-mediated systems shape learning behavior, the study contributed to psychological understandings of autonomy in digital learning environments and provided a theoretically grounded perspective relevant to the design of autonomy-supportive AI-enhanced educational systems.
VL - 15
IS - 3
ER -
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