Cognitive Prosthetic: An AI-Enabled Multimodal System for Episodic Recall in Knowledge Work

• URL: http://arxiv.org/abs/2603.02072v1
• Date: Mon, 02 Mar 2026 16:58:53 GMT
• Title: Cognitive Prosthetic: An AI-Enabled Multimodal System for Episodic Recall in Knowledge Work
• Authors: Lawrence Obiuwevwi, Krzysztof J. Rechowicz, Vikas Ashok, Sachin Shetty, Sampath Jayarathna,
• Abstract summary: This paper presents an AI-enabled proof-of-concept designed to support episodic recall in knowledge work through structured episodic capture and natural language retrieval.<n>We present CPMS as a functional proof-of-concept demonstrating the technical feasibility of transforming heterogeneous sensor data into queryable episodic memories.<n>This work contributes an end-to-end, privacy-aware architecture for AI-enabled memory augmentation in workplace settings.
• Score: 6.688814515265279
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Modern knowledge workplaces increasingly strain human episodic memory as individuals navigate fragmented attention, overlapping meetings, and multimodal information streams. Existing workplace tools provide partial support through note-taking or analytics but rarely integrate cognitive, physiological, and attentional context into retrievable memory representations. This paper presents the Cognitive Prosthetic Multimodal System (CPMS) --an AI-enabled proof-of-concept designed to support episodic recall in knowledge work through structured episodic capture and natural language retrieval. CPMS synchronizes speech transcripts, physiological signals, and gaze behavior into temporally aligned, JSON-based episodic records processed locally for privacy. Beyond data logging, the system includes a web-based retrieval interface that allows users to query past workplace experiences using natural language, referencing semantic content, time, attentional focus, or physiological state. We present CPMS as a functional proof-of-concept demonstrating the technical feasibility of transforming heterogeneous sensor data into queryable episodic memories. The system is designed to be modular, supporting operation with partial sensor configurations, and incorporates privacy safeguards for workplace deployment. This work contributes an end-to-end, privacy-aware architecture for AI-enabled memory augmentation in workplace settings.

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