Large Language Models as Digital Coaches: Goal Commitment and Productivity in Flexible Work Arrangements
Keywords:
large language models, digital coaching, goal commitment, flexible work, algorithmic management, human–AI interaction, gig economy, fairnessAbstract
The rapid expansion of flexible work arrangements—ranging from remote corporate employment to platform-mediated gig labor—has heightened the need for scalable, personalized interventions that sustain goal commitment and productivity outside traditional managerial oversight. Concurrently, large language models have matured to a point where they can serve as conversational agents capable of nuanced coaching dialogues, goal elicitation, and progress monitoring. This paper examines large language models as digital coaches through a systems lens, integrating perspectives from human–computer interaction, organizational psychology, algorithmic management, and sociotechnical infrastructure. We trace how goal commitment theory and self-determination principles inform the design of coaching interactions, then analyze the architectural components that enable an LLM-based coach to operate across diverse work contexts. The discussion foregrounds system-level trade-offs including accuracy–personalization tensions, hallucination risks, prompt injection vulnerabilities, and the scaling challenges of maintaining user-specific goal models. We develop a governance framework encompassing fairness audits, transparency mechanisms, data sovereignty protections, and the avoidance of deskilling, all situated within the evolving regulatory landscape for AI in employment. Deployment considerations address sustainability footprints, edge-cloud computation split, and integration with existing digital labor platforms. The paper also probes the policy implications of algorithmic goal steering in gig work, contrasting self-set goals with platform-imposed targets, and extrapolates from a recent field experiment that demonstrated the productivity effects of self-determined goal setting among gig workers. The conclusion articulates a research and design agenda that insists on robust, equitable, and worker-centric LLM coaching systems as flexible work becomes a permanent feature of the global labor market.
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