The aim of this article is to describe a method that helps analysts to translate qualitative data gathered in the field, collected for the purpose of requirements specification, to a model usable for software engineers. Requirements specification constitutes three different parts: functional requirements, quality requirements, and nonfunctional requirements. The first one specifies how the software system should function, who are the actors, and what are the input and output of the functions. The second one specifies what quality requirements the software should meet while operating in context of its environment such as reliability, usability, efficiency, portability, and maintainability. Finally, the third part specifies other requirements including context of use and development constraints. Examples of context of use are where and when the system is used, and examples of development constraints are human resources, cost and time constraints, technological platforms, and development methods. The role of the requirements specification is to give software engineers a basis for software design, and, later in the software development life cycle, to validate the software system. Requirements specification can also serve the purpose of validating users’ or customers’ view of the requirements of the system. On one hand, there has been a growing trend towards analyzing needs of the user and abilities through participatory design (Kuhn & Muller, 1993), activity theory (Bertelsen & Bødker, 2003), contextual design (Beyer & Holtzblatt, 1998), user-centered design (Gulliksen, Göransson, & Lif, 2001), and co-design and observation as in ethnography (Hughes, O’Brien, Rodden, Rouncefield, & Sommerville, 1995). Common to these methods is that qualitative data (Taylor & Bogdan, 1998) is collected, to understand the future environment of the new system, by analyzing the work or the tasks, their frequency and criticality, the cognitive abilities of the user and the users’ collaborators. The scope of the information collected varies depending on the problem, and sometimes it is necessary to gather data about the regulatory, social, and organizational contexts of the problem (Jackson, 1995) to be solved. The temporal and spatial contexts describe when the work should be carried out and where. On the other hand, software engineers have specified requirements in several different modelling languages that range from semiformal to formal. Examples of semiformal languages are UML (Larman, 2002), SADT, and IDEF (Ross, 1985). Examples of the latter are Z (Potter, Sinclair, & Till, 1996), VDM or ASM. Those are modelling languages for software development, but some languages or methods focus on task or work modelling such as Concurrent Task Trees (Paterno, 2003) and Cognitive Work Analysis (Vicente, 1999). Others emphasize more the specification method than a modelling language. Examples of the former are Scenario-Based Design (SBD) (Rosson & Carroll, 2002) and Contextual Design (Beyer & Holtzblatt, 1998). In practice, many software developers use informal methods to express requirements in text, e.g., as narrations or stories. Agile Development Methods (Abrahamsson, Warsta, Siponen, & Ronkainen, 2003) emphasize this approach and thereby are consistent with their aim of de-emphasizing methods, processes, and languages in favor of getting things to work. A popular approach to requirements elicitation is developing prototypes. There has been less emphasis on bridging the gap between the above two efforts, for example, to deliver a method that gives practical guidelines on how to produce specifications from qualitative data (Hertzum, 2003). One reason for this gap can be that people from different disciplines work on the two aspects, for example, domain analysts or experts in HCI, and software engineers who read and use requirements specification as a basis for design. Another reason for this gap may be in the difference in the methods that the two sides have employed, that is, soft methods (i.e., informal) for elicitation and analysis and hard methods (i.e., formal) for specification. In this article, we suggest a method to translate qualitative data to requirements specification that we have applied in the development of a Smart Space for Learning. The process borrows ideas from or uses scenarios, interviews, feature-based development, soft systems methodology, claims analysis, phenomenology, and UML.