Life’s What Happens When You’re Planning: How People Use AI to Map Their Futures

Author: Denis Avetisyan

New research explores how individuals are leveraging AI chatbots like ChatGPT to tackle long-term life planning, and where these tools fall short.

A planned trajectory, visualized as a primary green line, navigates a complex decision space where divergent gray branches represent alternative pathways and the inherent risk of unsuccessful outcomes.

This study investigates the use of Large Language Models for long-term task planning, revealing challenges with personalization, uncertainty, and the need for more robust cognitive scaffolding.

While effective long-term planning requires anticipating both success and failure, the role of emerging AI tools in supporting this inherently uncertain process remains largely unexplored. This study, ‘Your plan may succeed, but what about failure? Investigating how people use ChatGPT for long-term life task planning’, examines how individuals utilize ChatGPT for complex, future-oriented goals, revealing that the tool functions as helpful cognitive scaffolding despite limitations in personalization and realistic uncertainty handling. Our interview-based research demonstrates that users actively adapt AI-generated outputs, seeking systems that offer trustworthy guidance and acknowledge the potential for unforeseen challenges. How can we design AI planning systems that truly support evolving human-AI collaboration in the face of life’s inevitable uncertainties?

The Fragility of Distant Intentions

Conventional task management systems are frequently optimized for short-term, well-defined objectives, leaving individuals ill-equipped to address the ambiguities inherent in long-term aspirations. These systems typically demand precise deadlines and clearly delineated steps, a structure that clashes with the fluid nature of distant goals where unforeseen obstacles and evolving priorities are the norm. This mismatch often leads to frustration, as plans built on initial assumptions require frequent, disruptive revisions, or worse, become irrelevant before completion. The further one attempts to plan into the future, the greater the potential for these uncertainties to accumulate, rendering traditional checklists and rigid schedules ineffective and fostering a sense of being overwhelmed by the sheer scale of the undertaking. Consequently, individuals may avoid long-term planning altogether, prioritizing immediate tasks over the pursuit of more substantial, yet less certain, objectives.

The human mind, while capable of remarkable feats, faces inherent limitations when confronted with extensive, multi-step plans. Each additional task and dependency within a long-term project exponentially increases cognitive load – the total mental effort required to hold information in working memory and manipulate it. This burden isn’t simply about the quantity of tasks, but the intricate web of relationships between them; anticipating how completing one step affects subsequent ones demands considerable mental resources. Consequently, individuals often experience analysis paralysis, feeling overwhelmed by the sheer scope of the undertaking. This frequently manifests as procrastination – delaying action due to the perceived difficulty – or, ultimately, complete abandonment of the plan, as the cognitive strain outweighs the perceived benefits of achieving the distant goal. The brain, seeking to conserve energy, often prioritizes immediate, less demanding tasks over complex, long-term objectives, highlighting a fundamental challenge in sustained planning.

Truly effective long-term planning transcends simply outlining desired actions; it necessitates a proactive engagement with potential disruptions. Research indicates that individuals who routinely consider plausible setbacks – identifying potential obstacles and formulating contingency plans – demonstrate significantly higher rates of goal attainment. This isn’t merely pessimistic forecasting, but rather a cognitive strategy that reduces the paralyzing effect of unexpected challenges. By pre-emptively mapping out alternative routes and resource allocations, planners mitigate the emotional and practical burdens of crisis management, fostering resilience and maintaining momentum even when circumstances deviate from the ideal. The ability to anticipate ‘what if’ scenarios transforms long-term objectives from fragile aspirations into robust, adaptable strategies.

AI as a Scaffold for Actionable Intent

ChatGPT facilitates task decomposition by functioning as an interactive planning assistant. Rather than simply generating a list of steps, the model engages in a conversational exchange to understand the user’s overarching goal and then iteratively breaks it down into smaller, more readily achievable subtasks. This process allows for dynamic refinement of the plan based on user feedback and changing circumstances. The model doesn’t just provide a plan; it actively participates in its creation, enabling users to clarify ambiguities and address potential obstacles at each stage of decomposition. This interactive approach contrasts with static planning tools and supports a more flexible and adaptable problem-solving methodology.

The interactive nature of large language models facilitates a scaffolding approach to task completion by providing support at each stage of a defined plan. This is achieved through a conversational interface where users can request progressively detailed instructions, receive feedback on completed steps, and iteratively refine the plan as needed. By breaking down complex goals into smaller, manageable sub-tasks and offering guidance on each, the model effectively distributes the cognitive load, reducing the amount of information a user must hold in working memory. This process mirrors traditional scaffolding in education, where temporary support structures are provided to assist learners, and gradually removed as competence increases, allowing users to independently achieve their objectives.

The quality of output from large language models is directly correlated with the specificity and clarity of the input prompt. Detailed prompts, outlining desired format, length, and specific constraints, consistently yield more actionable and relevant responses. Furthermore, iterative prompt refinement – analyzing model outputs and adjusting prompts accordingly – is essential for tailoring the model’s scaffolding to individual user needs and task complexities. Providing contextual information within the prompt, such as user expertise level or desired outcome granularity, further enhances the model’s ability to deliver appropriately scaled and focused support.

Uncertainty: The Dual Challenge of Long-Term Vision

Long-term planning is inherently challenged by two distinct forms of uncertainty. Action uncertainty refers to the ambiguity surrounding the optimal course of action to achieve a desired goal; multiple paths may exist, and the most effective one is not immediately apparent. Complementing this is outcome uncertainty, which describes the unpredictability of consequences, even when a specific action is chosen; external factors and unforeseen circumstances can prevent a planned outcome, regardless of the quality of the decision-making process. Both action and outcome uncertainty increase in complexity and impact over extended time horizons, necessitating adaptive strategies and contingency planning.

Integrating ChatGPT with failure prediction techniques enables proactive risk assessment in long-term planning. These techniques, leveraging historical data and predictive modeling, identify potential points of failure within a proposed plan. ChatGPT then utilizes this information to simulate scenarios, assess the likelihood of negative outcomes, and generate alternative strategies to mitigate identified risks. This process doesn’t eliminate uncertainty, but provides users with a broadened range of options and a data-informed basis for decision-making, allowing for adjustments before encountering actual roadblocks. The system can evaluate multiple pathways, considering various constraints and objectives, to suggest plans with increased robustness and a higher probability of successful completion.

Research into the application of ChatGPT for long-term planning has yielded qualitative data regarding user interaction patterns. Analysis of user prompts indicates a tendency to seek both practical advice and broader perspective when outlining future tasks. Identified sources of uncertainty commonly relate to external factors – such as economic shifts or unforeseen events – and internal factors like evolving personal priorities. Participants consistently described ChatGPT as fulfilling two primary roles: a scaffold, providing structured suggestions and organizational assistance, and a reflective partner, enabling users to articulate and re-evaluate their goals and assumptions through conversational interaction. These findings highlight the nuanced ways individuals integrate AI into their planning processes, extending beyond simple task automation to encompass cognitive support and self-reflection.

The Resilience of Adaptive Strategies

ChatGPT enables a dynamic approach to planning by constantly evaluating progress and refining strategies in response to new information. Rather than relying on a static, pre-defined plan, the system functions as an iterative problem-solver, continuously comparing actual outcomes against projected goals. This ongoing monitoring allows ChatGPT to identify deviations early, reassess priorities, and adjust the plan accordingly – effectively mitigating the impact of unforeseen challenges or shifting circumstances. The result is a more resilient and effective planning process, capable of navigating real-world complexity and maximizing the probability of success even when initial assumptions prove inaccurate. This continuous feedback loop transforms planning from a predictive exercise into an adaptive response, fostering a proactive and flexible methodology.

The capacity to refine plans through repeated cycles of action and assessment fundamentally diminishes the influence of initial uncertainties on overall success. Rather than relying on a single, potentially flawed, pre-determined course, an iterative approach allows for continuous recalibration based on real-world feedback. This dynamic process isn’t simply about correcting errors; it actively leverages emerging information to improve the plan’s trajectory, making it more robust and resilient to unforeseen challenges. Consequently, outcomes are less dependent on predicting every variable at the outset and more reliant on the system’s ability to adapt and optimize its strategy as conditions evolve, thereby substantially increasing the probability of achieving the desired results.

The successful integration of artificial intelligence into complex planning relies heavily on the implementation of explainable AI (XAI) features. Beyond simply delivering a revised plan, these features illuminate the reasoning behind the adjustments, fostering crucial user trust. When a system can articulate why it modified a strategy – citing specific data points, identified roadblocks, or newly considered variables – it moves beyond being a ‘black box’ and becomes a collaborative partner. This transparency is not merely about satisfying curiosity; it’s about enabling informed oversight, facilitating error detection, and ultimately ensuring that the AI’s recommendations align with human values and objectives. Without such explainability, even a highly effective AI risks being perceived as unreliable, hindering its adoption and limiting its potential for real-world impact.

The research illuminates how current Large Language Models, while capable of providing a framework for long-term planning, often fall short in addressing the inherent unpredictability of life. This echoes Robert Tarjan’s observation: “Complexity is not a bug, it’s a feature.” The study reveals that users readily adopt ChatGPT as cognitive scaffolding, yet its rigid structure struggles to adapt to nuanced personal contexts or unforeseen circumstances. This highlights the critical need for AI systems that embrace complexity – systems that move beyond simple task lists to model uncertainty and facilitate genuinely adaptive planning, acknowledging that failure scenarios are as crucial as successful ones. The elegance of a truly effective system lies not in eliminating complexity, but in managing it effectively.

The Road Ahead

The apparent success of employing a large language model for life planning feels, upon closer inspection, akin to building a magnificent clock without accounting for the irregularities of time itself. This work reveals that while ChatGPT offers a useful – if somewhat brittle – cognitive scaffolding, its inherent limitations in handling genuine uncertainty and tailoring advice to the nuances of individual circumstance remain profound. The system excels at generating steps, but struggles to anticipate – or even acknowledge – the inevitable deviations from the planned path.

Future development must move beyond simply increasing the scale of these models. The challenge isn’t just about creating a more verbose heart, but understanding the entire circulatory system. A truly adaptive planning assistant will require mechanisms for incorporating feedback, acknowledging the probabilistic nature of future events, and – crucially – representing a model of the user’s evolving values and priorities.

One suspects the most significant advances will not arise from improvements to the language model itself, but from a deeper understanding of the interface between human intention and algorithmic suggestion. The goal is not to outsource agency, but to augment it; to build tools that assist in navigating complexity, not dictate a pre-ordained course. The temptation to engineer certainty must be resisted; a wise system embraces the inherent messiness of life.

Original article: https://arxiv.org/pdf/2512.11096.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

The Fragility of Distant Intentions

AI as a Scaffold for Actionable Intent

Uncertainty: The Dual Challenge of Long-Term Vision

The Resilience of Adaptive Strategies

The Road Ahead

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