The Algorithmic Learner: How Platform Logic Shapes Gen-Z's Attention, Motivation, and Wellbeing

  1. Sayed Mahbub Hasan Amiri

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Abstract

As learning settings become more intermediated by algorithm-based platform, a new “invisible curriculum” takes shape that organizes the cognitive and affective substrates of Generation Z. This paper examines the way in which the embedded logic of these platforms (that is, the way they are designed to be quantified, to predictively recommend, and to engage) shapes attention, motivation and psychosocial well-being. Drawing on educational psychology and platform studies, we apply a mixed-method approach that combines critical architectural analysis, survey data (n=XXX), and semi-structured interviews (n=XX), with interpretation through the conceptual perspectives of attentional economics and digital nudge theory. Results suggest that algorithmic curation sustains short-cycle attention and prioritises fast interaction over slow consumption, cultivating motivational states that are attentive to notifications and micro-feedback loops. Adaptive elements can tailor learning experiences, but also promote compulsive checking, emotional volatility, and a decrease in intrinsic motivation for lengthy undertakings underscoring a fundamental tension between platform effectiveness and cognitive health. They go on to say that “platform logics” infuse educational institutions as a force that impacts them systemically. The talk will make the case for, and present specific design principles and policy mechanisms for, healthier, learner-centred digital ecosystems that re-enable agency of the algorithmic learner.

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  1. PREreview

    This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17980899.

    This preprint presents a timely and important investigation into the psychological and cognitive impacts of algorithmically mediated learning platforms on Generation Z. The interdisciplinary approach, combining platform studies, educational psychology, and digital sociology, is a significant strength. The core argument that platform logic creates an "invisible curriculum" affecting attention, motivation, and wellbeing is compelling and well-contextualized. The paper is clearly a work in progress, as indicated by placeholders (n=XXX), but the proposed methodology and conceptual framework provide a solid foundation for impactful research.

    Major issues

    Major Strengths:

    1. Salient and Interdisciplinary Topic: The research question addresses a critical gap at the intersection of education, digital media studies, and mental health. Focusing on the embedded "logic" of platforms, rather than just their surface features, provides a more profound analytical lens.

    2. Robust Conceptual Framework: The use of "attentional economics" and "digital nudge theory" is appropriate and offers a sophisticated way to theorize how design choices translate into cognitive and affective outcomes. The concept of the "invisible curriculum" is particularly effective.

    3. Methodological Triangulation: The proposed mixed-methods approach (architectural analysis, surveys, interviews) is well-suited to capture both the structural mechanisms of platforms and the lived experiences of learners. This design promises rich, nuanced data.

    4. Balanced Analysis: The acknowledgment of both the potential benefits (e.g., adaptive tailoring) and the significant risks (e.g., compulsive checking, erosion of intrinsic motivation) demonstrates a nuanced understanding of the technology.

    Major Weaknesses and Areas for Development:

    1. Lack of Empirical Data: As a preprint, the core findings are presented as hypothetical results ("Results suggest that..."). The review is necessarily based on the proposal and argument. The validity of the conclusions hinges entirely on the yet-to-be-presented data collection and analysis. The placeholders for sample sizes (n=XXX, n=XX) need to be filled with details sufficient to judge statistical power and qualitative saturation.

    2. Definition and Scope of "Platform Logic": While the term is central, a more granular breakdown of its constitutive elements would strengthen the analysis. For example, distinguishing between the logic of quantification (metrics, gamification), prediction (recommendation engines), and engagement (notification systems, variable rewards) and then mapping these specifically to the measured outcomes (attention, motivation, wellbeing) would create a clearer analytical matrix.

    3. Operationalization of Key Variables: The manuscript would benefit from a more detailed preview of how the dependent variables especially "psychosocial well-being" and "intrinsic motivation" will be measured. Which validated scales will be used in the survey? How will these complex constructs be explored in interviews?

    4. Generational Specificity: The focus on Gen Z is justified, but the argument could be sharpened by more explicitly contrasting the platform logic's impact on Gen Z with prior generations' learning environments (e.g., pre-digital, early web). This would better isolate what is unique about the current "algorithmic" phase.

    5. Structural Clarity: The final sentence mentions "design principles and policy mechanisms" but these are not outlined, even preliminarily. Given their importance to the paper's concluding impact, a section sketching the proposed direction of these recommendations would be valuable.

    Minor issues

    • The phrase "fast interaction over slow consumption" is evocative. Clarifying what constitutes "slow consumption" in a learning context (e.g., deep reading, sustained project work) would be helpful.

    • The term "emotional volatility" is a strong claim. It will be important for the methods to carefully define and measure this, potentially linking it to specific platform triggers (e.g., feedback loops, social comparison features).

    • A brief discussion of ethical considerations for the research (especially regarding interviewing minors about wellbeing and platform use) would be a prudent addition to the methods section.

    Significance and Potential Impact: If executed with rigor, this study has high potential to make a significant contribution to several fields. It can inform:

    • Educational Design: Providing evidence for the development of more humane, learner-centric edtech.

    • Digital Policy: Contributing to the growing discourse on regulating algorithmic systems, especially for vulnerable populations like students.

    • Theoretical Scholarship: Advancing the conceptual integration of platform studies with cognitive and educational psychology.

    This preprint outlines a highly relevant and theoretically sophisticated research project. Its main current limitation is the absence of results, which is expected at this stage. The authors are encouraged to proceed with the empirical work, paying close attention to the precise operationalization of their key constructs and the granular analysis of how specific platform mechanisms yield specific psychological outcomes. Upon completion of data collection and analysis, this work is likely to be a valuable addition to the literature on technology, cognition, and education.

    Competing interests

    The author declares that they have no competing interests.

    Use of Artificial Intelligence (AI)

    The author declares that they did not use generative AI to come up with new ideas for their review.

  2. Version published to 10.20944/preprints202512.1589.v1