Typology 1: The Director
(High steering + in-depth understanding)
The Director is situated in the quadrant of high steering and in-depth understanding. It is the typology that implicitly defines both sessions as the professional standard, whilst at the same time representing the most demanding position in terms of competencies.
What characterises the Director?
The professional actively guides AI within clear professional frameworks, whilst possessing sufficient in-depth understanding of the domain and processes to interpret the output in terms of content, make adjustments and justify it professionally. AI speeds up the work and increases production capacity, but the professional monitors the direction and quality and retains ultimate responsibility.
The steering is not intuitive or merely operational here, but conscious, purposeful and well-founded. The Director defines in advance the context, objectives, quality criteria and expectations within which AI operates, and actively adjusts the process when the output does not sufficiently align with the professional’s intent. This fundamentally distinguishes this role from the Operator.
How does this manifest in the two sessions?
In the CMM session, the Director appears as the professional who interprets a briefing beyond the literal question, defines a creative concept based on their own perspective and signature style, and uses AI to generate variations. The professional then selects, positions and justifies that output based on substantive insight. The most concrete illustration of this comes from Wouter (Yungo): the professional who oversees the ‘umbrella concept’ from which AI derives millions of personalised variants. The execution is largely automated; the framework remains entirely human.
In the TI session, the Director is the developer who uses AI for code generation, boilerplate and testing, but who independently justifies architectural choices, understands and assesses generated solutions, and assumes full responsibility in production environments. The statement “the moment something goes into production, AI ceases to exist” perfectly encapsulates the Director standard: at critical moments, the professional must be able to analyse and decide independently, and remain in charge of the final solution.
Why is this the most demanding role?
The Director combines two dimensions of competence, each of which is demanding in its own right. At the same time, this role requires professional qualities that encourage the consistent application of critical judgement, responsibility and active steering, even under time pressure or when faced with convincing AI output. A high level of steering requires clarity regarding objectives, context and quality criteria, as well as sufficient AI literacy to manage AI systems in a conscious and purposeful manner. In-depth understanding demands strong conceptual knowledge of the domain and the underlying professional processes. Mastering both simultaneously – even under time pressure and in complex situations – is what sets this role apart.
Furthermore, the complexity faced by the Director is not disappearing, but is changing form. The volume of operational work is decreasing, whilst assessment, guidance and accountability are becoming more important. This is cognitively more demanding than it appears at first glance, particularly as AI output becomes increasingly convincing and the temptation grows to intervene less actively or to engage in critical reflection.
What do the sessions reveal about its feasibility?
Both sessions are candid about the tension associated with this role. Participants describe the Director as a professional standard, but at the same time highlight how difficult it is to sustain this role in practice over the long term.
Within CMM, the question arises as to how creative individuality, depth of content and professional distinctiveness can be preserved as production pressures increase and AI output becomes ever faster and more credible. Within TI, there is explicit concern that knowledge development will be stunted if junior staff use AI without developing the conceptual foundation needed to make architectural choices independently and analyse complex problems. The concern that AI primarily automates entry-level and routine work, thereby putting career progression pathways for junior staff under pressure and making it more difficult to build expertise, makes this tension structurally apparent.
Implications for the curriculum
The Director constitutes the intended final profile of the programme within an AI-integrated professional context. This has direct implications for curriculum choices.
It is not enough simply to teach students how to work with AI applications or to equip them with operational AI skills. Professional autonomy demands more than merely the instrumental use of AI. Curricula must therefore strengthen the conceptual foundation upon which professional practice is based. This encompasses in-depth understanding of the subject area and processes, critical judgement, and the professional dispositions that enable students to actually exercise this judgement and assume this responsibility when AI takes over a large part of the operational work.
At the same time, students must develop sufficient AI literacy to manage AI systems in a conscious, purposeful and professional manner. This involves not only using AI, but also being able to contextualise, direct and actively adjust AI-integrated work processes.
The central question for WP3 (Design of educational scenarios) is therefore how learning pathways can guide students from a natural starting point – often comparable to the Operator – towards the Director profile, without regressing towards the Dependent.
At the same time, this typology raises important questions about evaluation and assessment within AI-integrated learning environments. As professional added value increasingly shifts towards understanding (steering), critical evaluation and responsibility, it is no longer sufficient to assess students solely on the basis of their final product. Programmes are therefore challenged to develop forms of assessment that demonstrate the extent to which students not only possess the required knowledge and skills, but also apply them in a purposeful and responsible manner within AI-integrated work processes.