Search arXiv⌕ Search

arXiv · 2610.02731

Same Performance, Different Process: Epistemic Ownership in AI-Mediated Education

Abstract

Generative AI weakens the link between assessed performance and the cognitive processes that educational work is expected to reflect. This paper examines observable traces of those processes in a sample of 150 student-AI conversations. The analysis uses the concept of epistemic ownership as an interpretive framework, focusing on three dimensions that can become visible in interaction: Direction, Integration, and Evaluation. The results show that similar assessed performance can coexist with substantially different observable patterns of cognitive participation. Assignment scores therefore provide information about assessed outcomes but do not recover how cognitive work was distributed between student and AI during the interaction. The study provides an empirical basis for asking how assessment should incorporate evidence about cognitive process, rather than relying exclusively on the quality of the resulting product.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jorge Fábrega. 2026-10-02. Same Performance, Different Process: Epistemic Ownership in AI-Mediated Education. https://arxiv.org/abs/2610.02731

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Large Language Models and Augmented Democracy

Artificial intelligence enables computational agents to represent political preferences and take part in collective decision-making. In this thesis, I investigate the opportunities and challenges of digital twins (DTs) based on Large Language Models (LLMs) as intermediaries in augmented democracy, focusing on individual preference representation, collective representation of political organizations, and the vulnerability of those representations to attackers. First, using data from an online experiment in Brazil, I examine whether personalized DTs can predict citizens' preferences for unseen policy proposals. Second, I extend the DT framework from individuals to political organizations. Using Swiss parliamentary data, I build topic-specific knowledge graphs from lawmakers' legislative records and connect them to LLM-based lawmaker agents, which are organized into party-level DTs representing collective positions. Agentic deliberation among these agents tests whether aggregated party representations capture a broader range of intra-party perspectives than official party communications. Finally, I study the vulnerability and robustness of LLM-mediated deliberation against prompt-injection attacks that amplify viewpoints, suppress opinions, or redirect consensus. Using data from a 2023 deliberative experiment in the United Kingdom, I analyze how attack effectiveness varies with the distribution of opinions and rhetorical strategies, and evaluate a pipeline combining injection detection, structured opinion representations, and reinforcement learning to improve resistance. These findings characterize the opportunities and challenges of LLM-based digital twins in augmented democracy, stressing accurate preference representation, faithful aggregation, and robustness to strategic interaction.

cs.CY↗

Enhancing Knowledge Tracing through Leakage-Free and Recency-Aware Embeddings

Knowledge Tracing (KT) aims to predict a student's future performance based on their sequence of interactions with learning content. Many KT models rely on knowledge concepts (KCs), which represent the skills required for each item. However, some of these models are vulnerable to label leakage, a phenomenon in which the input data inadvertently reveal the correct answer, particularly in datasets with multiple KCs per question. We propose a straightforward yet effective solution to prevent label leakage by masking ground-truth labels during input embedding construction whenever such leakage could occur. To accomplish this, we introduce a dedicated MASK label, inspired by masked language modeling (e.g., BERT), to replace ground-truth labels. In addition, we introduce Recency Encoding, which encodes the step-wise distance between the current item and its most recent previous occurrence. This distance is important for modeling learning dynamics such as forgetting, which is a fundamental aspect of human learning, yet it is often overlooked in existing models. Recency Encoding demonstrates improved performance over traditional positional encodings on multiple KT benchmarks. We show that incorporating our embeddings into KT models such as DKT, DKT+, AKT, and SAKT consistently improves prediction accuracy across multiple benchmarks. The approach is both efficient and widely applicable.

cs.CY↗

"Lighting The Way For Those Not Here": How Can Technology Researchers Help Resist the Missing and Murdered Indigenous Relatives (MMIR) Crisis?

Indigenous peoples across Turtle Island face disproportionate rates of disappearance and murder, a genocide rooted in settler-colonial violence and systemic erasure. Technology plays a crucial role in the Missing and Murdered Indigenous Relatives (MMIR) crisis: it perpetuates systemic violence and impedes investigations, yet also enables sites of advocacy, healing, and resistance. For example, Native communities utilize AMBER alerts, digital news, sovereign crowdsourced databases, social media groups, and resistance movements to mobilize searches, amplify awareness, and honor missing relatives. Yet little research in HCI has critically examined the role of technology in shaping the MMIR crisis. Thus, we qualitatively analyze 140 webpages to identify sociotechnical barriers that hinder communities' efforts, while highlighting actions that foster healing, safety, and resilience. We grounded our analysis in stories that resist epistemic erasure through relational accountability, critical humility, cultural sensitivity, and refusal. Finally, we provide recommendations for HCI to recognize self-determination and sovereignty of Indigenous technologies, direct action to support families, and honor Indigenous onto-epistemologies that cease epistemic violence.

cs.CY↗