The design of data collection scenarios is critical for evaluating intelligent systems for social intention recognition in aviation. Identical aircraft behaviors can generate multiple equally plausible intention interpretations depending on situational context and the observer’s professional perspective, yet existing research offers limited guidance for constructing scenarios that preserve this interpretive open-endedness. This study addresses this gap through an exploratory, literature-based investigation of how contextual factors shape intention interpretation across aviation roles. An integrated framework combining the 3Cs model of situational analysis and script theory is proposed to identify contextual dimensions influencing interpretation. Through qualitative synthesis of aviation literature, the framework demonstrates how variations in cues, classes, characteristics, and internal-external script configurations can produce divergent but valid intention narratives for the same observable behavior. The resulting scenario-first methodology provides structured guidance for designing aviation scenarios that support role-dependent intention annotation and evaluate intelligent systems. As a conceptual contribution, the framework requires empirical validation by aviation professionals.

Intelligent systems are being increasingly deployed alongside humans, yet their ability to understand human intentions in order to perform tasks better is far from perfect. In this paper a literature survey will be performed to obtain existing examples of narratives, with and without intelligent systems,that take place in a Hospital - a setting with a variation in situation openness, where a framework of cues, characteristics, classes, internal scripts, external scripts, and memory and associative triggers all play an equally important role in determining an individual’s intention. Dimensions were extract from these scenarios and were analysed based on the three roles: Patients, Clinicians, and Family Members. Results and findings were used to develop a new case study involving an intelligent system assisting a doctor with diagnosing a patient and communicating it to them. Variations of contextual cues, amid other dimensions, were made to demonstrate multiple plausible narratives and its effects on intention.

Intelligent systems in autonomous driving increasingly require the ability to infer social intentions to ensure safe and fluid interactions with human road users. However, current approaches typically frame this problem as objective trajectory prediction or fixed classification, ignoring the open-ended nature of human interpretation where a single physical behaviour can generate multiple plausible narratives. To address the gap between trajectory forecasting and narrative understanding, this research investigates how to systematically map the dimensions of variation in driving situations to the range of intention narratives they generate. A literature survey was conducted to distinguish between foundational human social norms and current algorithmic approaches. By integrating script theory with the 3Cs framework (Cues, Characteristics, Classes), this study developed a dimension extraction framework to analyse where objective observations diverge into subjective interpretations. Through comparative analysis of prototypical scenarios (lane merging and pedestrian negotiation), results revealed that current intelligent systems operate predominantly in geometric space, optimizing for physical feasibility, whereas human drivers operate in social space governed by normative scripts. The research concludes that narrative open-endedness is inversely proportional to the strength of physical and social constraints. That is, when constraints are weak, human internal scripts diverge from machine logic, leading to critical prediction errors. Consequently, future systems must shift from raw trajectory output to semantic narrative understanding to explicitly model this uncertainty and align machine reasoning with human expectations.