“Tell me how a project starts and I'll tell you how it ends.”
- Based on Euripides -

This course introduces the basics of project management in an AI-supported learning environment. Students learn key concepts such as project characteristics, project structures, and projects as problem-solving processes. The course also covers essential aspects of project documentation, contractual frameworks, and coordination in projects. In the tutorial, students apply these foundations in a collaborative, practice-oriented project task (e.g., developing a project management chatbot). AI is used as a supporting tool for learning, reflection, and structuring, while critical thinking and human judgment remain central.

AI-Supported Self-Study Unit

This section introduces the fundamental concepts of project management with a focus on the definition and classification of projects. It is designed as an AI-supported self-study unit that enables students to work independently and prepare for the subsequent lecture without the presence of an instructor. Artificial intelligence is used as a learning assistant to explain core concepts, illustrate differences between projects and non-project activities, and provide practical examples for classification. By engaging with AI-supported explanations and tasks, students build a shared conceptual foundation that allows in-class sessions to focus on discussion, application, and deeper analysis rather than introductory content.

Activity Outline

Activity 1: Conceptual Understanding – What Is a Project?

Students interact individually with an AI tool to explore the concept of a project.

Recommended AI Tools for this Activity:

• Microsoft Copilot — concept clarification and summarization
• Gemini — simple explanations and examples
• Academic Cloud (institutional platform for secure AI access)

Model Prompt 1:

“Think through the project ‘Moving House’ by answering the 7 essential planning questions.”

Students review the explanation and take brief notes focusing on:

• the identified problem,
• required project steps,
• estimated effort,
• assigned responsibilities,
• timeline,
• potential risks,
• missing tasks or issues.

Activity 2: Identifying Project Characteristics

Students deepen their understanding of typical project characteristics.

Recommended AI Tools for this Activity:

• ChatGPT — structured bullet points and concept explanation
• Notion AI — organizing and structuring key points
• Academic Cloud (institutional platform for secure AI access)

Model Prompt 2:

“List and briefly explain the key characteristics of a project.
Use bullet points and simple language.”

Students compare the AI output with their own understanding and note:

• which characteristics were already known,
• which aspects were new or unexpected.

Activity 3: Classification of Projects

Students explore basic approaches to classifying projects.

Recommended AI Tools for this Activity:

• ChatGPT — structured bullet points and concept explanation
• Microsoft Copilot — concept clarification and summarization
• Academic Cloud (institutional platform for secure AI access)

Model Prompt 3:

“Describe common ways to classify projects, for example by size, type, or object.
Provide short examples for each category.”

Students focus on high-level categories such as:

• small vs. large projects,
• product vs. service projects,
• improvement vs. development projects.

Detailed numerical thresholds are intentionally excluded.

Activity 4: Applied Classification Task

Students apply project criteria to everyday examples.

Recommended AI Tools for this Activity:

• Gemini — reasoning support and simplified categorization
• Microsoft Copilot — structured analysis and summaries
• Academic Cloud (institutional platform for secure AI access)

Model Prompt 4:

“Analyze the following activities and determine whether they are projects or not by using the 7 project characteristics:

• Organizing a birthday party
• Developing a new mobile app
• Daily production in a factory
• Moving to a new apartment”

Students document their decisions and reasoning in short written form.

Reflection and Responsible Use of AI

Students complete a short individual reflection (written notes, no grading).

Guiding Reflection Questions:

• Which aspects of project definition were clarified by AI?
• Where did you rely on your own judgment rather than the AI output?
• Were the AI explanations always clear and appropriate? Why or why not?
• How can AI support learning without replacing critical thinking?

Pedagogical and Operational Considerations

• The section supports self-paced, independent learning.
• AI provides immediate explanations and examples, reducing repetitive instruction.
• Students actively engage with concepts rather than passively consuming content.
• The activity is suitable for digitally supported learning environments.
• Initial guidance on responsible AI use is required.

Source: Deming, W.E. (1950): Deming Circle

1. What is the problem?
2. Which steps have to be taken?
3. How much effort is needed?
4. Who should do it?
5. When should it be done?
6. What could go wrong?
7. Is everything according to plan?

In this section, projects are examined from different conceptual perspectives in order to develop a deeper understanding of their nature and complexity. Projects are not only sequences of tasks but can be interpreted as complex systems and as structured problem-solving processes. These perspectives help explain why projects are inherently uncertain, why planning is challenging, and why systematic management approaches are required.

1. Projects as Complex Systems

Projects are first introduced as complex systems. From this perspective, a project transforms various inputs into outputs within a defined timeframe and under constrained resources. Inputs may include information, materials, fi-nancial resources, and human effort, while outputs typically consist of prod-ucts, services, or organizational changes.

Two system views are distinguished:

• Black Box Perspective
  In the black box view, the internal structure of the project is not fully transparent. The focus lies on the relationship between input and out-put, expressed as output = f(input). While this perspective highlights results, it provides limited insight into the internal causes of devia-tions, risks, or inefficiencies.
• Open Box Perspective
  In contrast, the open box view makes project processes visible. Tasks, responsibilities, timelines, resources, and progress are explicitly de-fined and monitored. Tools such as work breakdown structures or Gantt charts support this transparency. This perspective allows project managers to analyze interdependencies, identify bottlenecks, and ac-tively control project execution.

Use of AI (Instructor-Supported):

Artificial intelligence can support this perspective by generating simplified system visualizations, illustrating input–output relationships, or summarizing complex project structures into accessible representations. Short AI-generated explainer videos may be used to visualize black box and open box concepts.

2. Projects as Problem-Solving Processes

In a second perspective, projects are understood as structured problem-solving processes. A clear distinction is made between tasks, problems, and projects:

• A task is routine and well-defined.
• A problem involves uncertainty and requires analysis.
• A project represents a temporary, goal-oriented process designed to solve a complex problem.

From this viewpoint, projects follow a logical sequence: understanding the situation, identifying causes, developing solutions, implementing measures, and evaluating results. This aligns with established problem-solving frame-works such as structured root-cause analysis and continuous improvement cycles.
Projects are therefore characterized not only by their goals and deadlines but also by their role in reducing uncertainty and creating new solutions.

Use of AI (Instructor-Supported):
AI tools can assist in summarizing problem-solving models, comparing differ-ent frameworks, or demonstrating cause-and-effect relationships (e.g. through AI-generated diagrams or structured explanations).

Activity 5: Applied Classification Task

Students apply the following prompt to classify a project.

Recommended AI Tools for this Activity:

• ChatGPT Edu — concept comparison and differentiation 
• Microsoft Copilot — structured analysis and summaries 
• Academic Cloud (institutional platform for secure AI access)

Model Prompt 5:

“Based on the 7 characteristics of a project, please determine the differ-ences between the following items:
Task, Problem, Problem Solving Process, and Project”

Pedagogical and Operational Considerations

• The section supports instructor-led knowledge transfer combined with AI-enhanced explanations.
• AI is used to visualize abstract concepts such as complex systems and problem-solving processes.
• Short AI-generated summaries or quizzes may be applied to reinforce conceptual understanding.
• The approach reduces preparation effort for illustrative materials while maintaining academic rigor.
• Human oversight remains essential to ensure accuracy, contextual relevance, and alignment with learning outcomes.
   

Activity 1: Conceptual Understanding – What Is a Project?

Learning focus:
Students interact individually with an AI tool to explore the concept of a project.

Reflection Prompt:

“Think through the project ‘Moving House’ by answering the 7 essential planning questions.”

Students review the explanation and take brief notes focusing on:

• the identified problem,
• required project steps,
• estimated effort,
• assigned responsibilities,
• timeline,
• potential risks,
• missing tasks or issues.

• Better understanding of the problem
• Solutions can be planned specifically
• Logical sequence of steps becomes clear
• Time and costs can be planned
• Realistic planning and scheduling
• Possible risks and bottlenecks are transparent
• Effective reaction in case of deviations

1. Clarity of Objectives
2. Uniqueness of the Task
3. Difficulty of the Task
4. Process Character
5. Time Constraints
6. Team work
7. Resource Limitations

Activity 2: Project Characteristics – What Is NOT a Project?

Learning focus:
Students deepen their understanding of typical project characteristics.

Reflection Prompt:
“List and briefly explain the key characteristics of a project.
Use bullet points and simple language.”

Students compare the AI output with their own understanding and note:

• which characteristics were already known,
• which aspects were new or unexpected.

Size

• Time, Costs, People …

Subject

• Product, Service, Formula …

Type

• Improvement, R&D, Invest …

Activity 3: Classification of Projects: Finding Examples!
Learning focus:

Students explore basic approaches to classifying projects and find examples to determine the different project categories, e.g. “Project Size”: New Manufacturing Site vs. New Machine for Production

Reflection Prompt:

“Describe common ways to classify projects, for example by size, type, or object.
Provide short examples for each category.”

Students focus on high-level categories such as:

• small vs. large projects,
• product vs. service projects,
• improvement vs. development projects.

Detailed numerical thresholds are intentionally excluded.

ISO 69901 & ISO 21500

“Project is a temporary endeavor designed to produce a unique product, service or result with a defined beginning and end (usually time-constrained, and often constrained by funding or staffing) undertaken to meet unique goals and objectives, typically to bring about beneficial change or added value.”

“Project Management is the practice of initiating, planning, executing, controlling, and closing the work of a team to achieve specific goals and meet specific success criteria at the specified time. The primary challenge of project management is to achieve all of the project goals within the given constraints.”

Activity 4: Applied Classification Task: Do we have a Project?
Learning focus:

Students apply project criteria to given examples in order to find out, if we have a project (or not)

Reflection Prompt:

“Analyze the following activities and determine whether they are projects or not by using the 7 project characteristics:

• Organizing a birthday party
• Developing a new mobile app
• Daily production in a factory
• Moving to a new apartment”

Students document their decisions and reasoning in short written form.

In this section, projects are examined from different conceptual perspectives in order to develop a deeper understanding of their nature and complexity. Projects are not only sequences of tasks but can be interpreted as complex systems and as structured problem-solving processes. These perspectives help explain why projects are inherently uncertain, why planning is challenging, and why systematic management approaches are required.

1. Projects as Complex Systems

Projects are first introduced as complex systems. From this perspective, a project transforms various inputs into outputs within a defined timeframe and under constrained resources. Inputs may include information, materials, financial resources, and human effort, while outputs typically consist of products, services, or organizational changes.

Two system views are distinguished:

• Black Box Perspective
  In the black box view, the internal structure of the project is not fully transparent. The focus lies on the relationship between input and output, expressed as output = f(input). While this perspective highlights results, it provides limited insight into the internal causes of deviations, risks, or inefficiencies.

• Open Box Perspective
  In contrast, the open box view makes project processes visible. Tasks, responsibilities, timelines, resources, and progress are explicitly defined and monitored. Tools such as work breakdown structures or Gantt charts support this transparency. This perspective allows project managers to analyze interdependencies, identify bottlenecks, and actively control project execution.

Use of AI (Instructor-Supported):

Artificial intelligence can support this perspective by generating simplified system visualizations, illustrating input–output relationships, or summarizing complex project structures into accessible representations. Short AI-generated explainer videos may be used to visualize black box and open box concepts.

2. Projects as Problem-Solving Processes

In a second perspective, projects are understood as structured problem-solving processes. A clear distinction is made between tasks, problems, and projects:

• A task is routine and well-defined.
• A problem involves uncertainty and requires analysis.
• A project represents a temporary, goal-oriented process designed to solve a complex problem.

From this viewpoint, projects follow a logical sequence: understanding the situation, identifying causes, developing solutions, implementing measures, and evaluating results. This aligns with established problem-solving frameworks such as structured root-cause analysis and continuous improvement cycles.

Projects are therefore characterized not only by their goals and deadlines but also by their role in reducing uncertainty and creating new solutions.

Use of AI (Instructor-Supported):

AI tools can assist in summarizing problem-solving models, comparing different frameworks, or demonstrating cause-and-effect relationships (e.g. through AI-generated diagrams or structured explanations).

Activity 5: Applied Classification Task

Students apply the following prompt to classify a project.

Recommended AI Tools for this Activity:

• ChatGPT Edu — concept comparison and differentiation
• Microsoft Copilot — structured analysis and summaries
• Academic Cloud (institutional platform for secure AI access)

Model Prompt 5:

“Based on the 7 characteristics of a project, please determine the differences between the following items:

Task, Problem, Problem Solving Process, and Project”

Pedagogical and Operational Considerations

• The section supports instructor-led knowledge transfer combined with AI-enhanced explanations.
• AI is used to visualize abstract concepts such as complex systems and problem-solving processes.
• Short AI-generated summaries or quizzes may be applied to reinforce conceptual understanding.
• The approach reduces preparation effort for illustrative materials while maintaining academic rigor.
• Human oversight remains essential to ensure accuracy, contextual relevance, and alignment with learning outcomes.

Project as “Complex System“
Black Box vs. Open Box

Project as “Problem Solving Process“
Task vs. Problem vs. Project

Project as “Complex System“
Black Box vs. Open Box


Project as “Problem Solving Process“
Task vs. Problem vs. Project

Activity 5: Applied Classification Task: Task vs. Problem vs. Project

Learning focus:

Students apply the following prompt to classify a project.

Reflection Prompt:

Based on the 7 characteristics of a project, please determine the differences between the following items:Task, Problem, Problem Solving Process, and Project”

Students compare the AI output with their own understanding and note:

• which characteristics were already known,
• which aspects were new or unexpected.

“No matter how good the team or how efficient the methodology, if we´re not solving the right problem, the project fails.“ 
- H. Woody Williams -

This section structures the topic of contractual regulation in projects as presented in the course material. The focus lies on illustrating how expectations, agreements, and project documentation are systematically addressed within project planning. The section is designed as an instructor-led learning unit with clearly defined phases and targeted AI support.

Phase 1: Contractual Regulation in Projects

The section begins with an introduction to contractual regulation as a fundamental element of project organization. The instructor frames contracts as instruments that structure cooperation and define the relationship between involved parties.

The emphasis is placed on the role of contractual thinking in projects rather than on legal details.

AI support:

AI may be used to generate a concise overview slide or visual map showing how contractual regulation fits into the overall project planning process.

Phase 2: Project Expectations versus Reality

In this phase, the instructor addresses discrepancies between initial expectations and project reality. Illustrative examples from practice are used to demonstrate how differing perceptions arise during project execution.

This phase sensitizes students to the risks of unclear agreements and prepares the ground for structured project documentation.

AI support:

AI-generated visual examples or short quiz questions can be used to highlight typical expectation gaps and support awareness-building.

Phase 3: Project Expectations versus Results

Building on the previous phase, attention shifts to differences between expected and actual project results. Examples emphasize deviations in time, cost, and scope without entering quantitative detail.

The instructor uses this phase to reinforce the need for formalized agreements in project planning.

AI support:

AI may generate simplified comparison visuals or short recap prompts connecting expectations, execution, and outcomes.

Phase 4: Contractual Regulation through Project Documentation

This phase introduces project documentation as a means of contractual regulation. The instructor outlines how agreements are formalized and documented to create transparency and reference points throughout the project lifecycle.

The focus lies on structure and purpose, not on document content.

AI support:

AI tools may be used to visualize relationships between different project documents and their role in contractual regulation.

Phase 5: Project Assignment – Structure

The project assignment is introduced as a central structured document. The instructor presents its structure, emphasizing the separation into formal, technical, organizational, and legal components.

This phase highlights how information from different project roles contributes to the project assignment.

AI support:

AI may generate a structured outline or annotated example of a project assignment to support orientation and reduce explanation time.

Phase 6: Requirements versus Implementation Perspective

(Lastenheft vs. Pflichtenheft)

The section concludes by differentiating between requirement-oriented and implementation-oriented documentation. The instructor explains how this distinction supports clear communication and expectation alignment between stakeholders. The focus remains on understanding different perspectives, not on detailed specifications.

AI support:

AI-generated comparison tables or short knowledge checks may be used to reinforce the distinction.

Reflection and Responsible Use of AI

Students complete a short individual reflection (written notes, no grading).

Guiding Reflection Questions:

• How did AI-supported visualizations help clarify contractual structures in projects?
• Which aspects required interpretation beyond AI-generated input?
• Where do you see limitations of AI when dealing with agreements and expectations?

Pedagogical and Operational Considerations

• AI supports visualization, consolidation, and formative understanding checks.
• The instructor remains responsible for framing and interpretation.
• AI integration reduces preparation effort and supports consistent delivery.
• The section prepares students for later tutorials involving structured project documentation.

The Conflict:
Logical sequence of steps becomes clear Expectations of the Principal (Sponsor) do not agree with the Realization Ideas of the Agent (Manager)

Activity 6: Case Study Work: Why did the BER airport project fail?

Learning focus:

Students analyze the BER project using project management concepts and the Principal–Agent framework.

Investigation Prompt:

Analyze the project of the Berlin Brandenburg Airport with respect to deviations in time, cost, and quality, and explain these deviations using a Principal–Agent perspective by identifying the different expectations, incentives, and information asymmetries between project owners (principals) and contractors (agents).

Students will be able to:

• analyze the causes of project deviations
• examine conflicts between principals and agents
• reflect on how generative AI can support complex project analysis.

Formal Part
Place and date of issue, project number …

Technical Part
Project subject, technology, business need …

Organizational Part
Budget, milestones, project team …

Legal Part
Terms and conditions, signatures …

Basis: Jakoby, W. (2013), p. 77

Tender:

Customer´s Specification = Specification (e.g. requirement catalogue) describes the totality of the demands on the deliveries and services of an announced project.

Offer:

Contractor´s Specification = Specification (e.g. offer or functional spec) that describes the type and scope of the deliveries and services to which he/ she is committed.

Assumptions Asymmetric Information Opportunistic Behavior		Solutions Contract Controlling Hierarchy Incentives Culture Trust
Outcomes Adverse Selection Moral Hazard Hold-up

Basis: Kieser, A. / Ebers, M. (2019): Organisationstheorien, pp. 207-226

Activity 7: Case Study Work (2): What can we learn from the mistakes made in the BER project??

Learning focus:

Students analyze the BER project using project management concepts and the Principal–Agent framework.

Investigation Prompt:

Based on the analysis of the Berlin Brandenburg Airport project, identify the key lessons learned and propose improvements for future large-scale infrastructure projects, with particular focus on how Principal–Agent problems could be better managed through governance structures, contracts, and project management practices.

Students will be able to:

• Apply Principal–Agent theory to analyze governance problems in projects such as BER
• Identify key lessons from project failures, e.g. misalignment of expectations at the beginning
• Propose improvements for managing complex projects, e.g. effective incentives and trust.

This section introduces the Principal–Agent Theory as a theoretical foundation for understanding coordination and control challenges in projects. The theory is presented as an explanatory framework that supports earlier sections on contractual regulation and project organization. The section is designed as a concise, instructor-led unit with selective AI support.

Phase 1: Framing the Principal–Agent Relationship

The instructor introduces the principal–agent relationship as a typical constellation in projects, characterized by differing roles, interests, and levels of information. The focus lies on recognizing information asymmetry and the potential for conflicting objectives between involved parties.

AI support:

AI may be used to generate a simple visual representation of the principal–agent relationship to support conceptual understanding.

Phase 2: Assumptions and Consequences

The instructor outlines the core assumptions of the Principal–Agent Theory, such as asymmetric information and opportunistic behavior, and introduces typical consequences including adverse selection, moral hazard, and hold-up situations.

The emphasis lies on awareness rather than analytical depth.

AI support:

AI-generated summaries or short quizzes may be used to reinforce understanding of key terms and relationships.

Phase 3: Coordination and Control Mechanisms

The section then addresses typical coordination and control mechanisms discussed in the theory, such as contracts, incentives, and monitoring. These mechanisms are positioned as responses to the challenges identified in the principal–agent relationship.

AI support:

AI may generate structured overviews connecting assumptions, consequences, and coordination mechanisms.

Phase 4: Consolidation and Link to Project Management Practice

The instructor briefly consolidates the theory and highlights its relevance for understanding contractual structures and project governance discussed earlier in the module.

AI support:

AI may provide a concise recap or checklist summarizing the theoretical logic of the Principal–Agent Theory.

Reflection and Responsible Use of AI

Students are encouraged to briefly reflect on how AI-supported visualizations and summaries contributed to understanding the Principal–Agent Theory and where human interpretation remained essential.

Pedagogical and Operational Considerations

• The section is intentionally concise and theory-focused.
• The Principal–Agent Theory is used as an explanatory framework.
• AI supports visualization, summarization, and comprehension checks.
• The instructor remains responsible for interpretation and contextualization.

In this tutorial, students work on a real, practice-oriented project that requires them to apply the concepts and tools introduced in this module. The concrete project topic may change from year to year, for example the design of an own chatbot or the construction of a Varignon apparatus, but in all cases students collaboratively plan, document and manage an authentic project from definition to implementation.

This tutorial section focuses on the structured development of a project assignment for a group-based project. Students work in fixed groups of six on the development of a chatbot related to project management topics. The tutorial translates conceptual content from previous sections into applied project documentation.

The project assignment serves as a formal reference document that structures goals, expectations, scope, risks, and responsibilities within the group project.

Tutorial Structure and Work Sequence

Phase 1: Project Framing and Common Understanding

The tutorial begins with a short alignment phase within each group. Students clarify the general purpose of their chatbot project and establish a shared understanding of the project background.

AI support (content generation):

Students may use AI to articulate an initial project purpose in clear and structured language.

Recommended AI Tools for this Activity:

• ChatGPT Edu — concept comparison and differentiation
• Notion AI — structuring project documentation
• Academic Cloud (institutional platform for secure AI access)

Example Prompt:

“We are a student project group developing a chatbot related to project management.
Help us formulate a concise project purpose and background suitable for a project assignment.”

Phase 2: Definition of Project Goals and Success Criteria

In this phase, groups define what constitutes project success. The focus lies on formulating clear success criteria and identifying high-level project goals or milestones.

AI support (structuring and clarification):

AI may be used to check whether defined goals are clear, measurable, and consistent.

Recommended AI Tools for this Activity:

• ChatGPT Edu — concept comparison and differentiation
• Gemini — reasoning support and simplified categorization
• Academic Cloud (institutional platform for secure AI access)

Example Prompt:

“Review the following project goals and success criteria.
Identify unclear formulations and suggest improvements without adding new content.”

Phase 3: Project Timeline and Milestones

Groups sketch a rough project schedule and identify key milestones. The emphasis is on logical sequencing rather than detailed planning.

AI support (consistency check):

AI can be used to review the coherence between milestones and project goals.

Phase 4: Framework Conditions and Working Principles

Students define basic principles for collaboration, communication, and prioritization within the project team. These principles guide how the project is executed rather than what is produced.

AI support (reflection):

AI may be used to suggest typical project principles as a comparison basis, helping groups reflect on whether essential aspects are covered.

Phase 5: Risks, Limits, and Scope Definition

This phase focuses on identifying known risks and clearly distinguishing what is within and outside the project scope. The goal is to increase awareness of limitations and potential obstacles.

AI support (risk awareness):

AI may generate example risks for similar student projects, which groups compare with their own assessments.

Phase 6: Resources and Roles

Groups document required resources and define team roles, including project leadership and supporting functions.

AI support (role clarity):

AI may be used to review whether responsibilities are clearly assigned and whether role descriptions are consistent.

Learning Outcome of the Tutorial

By completing this tutorial, students:

• apply conceptual project management knowledge to a structured project document,
• experience the role of formal agreements in projects,
• practice responsible and reflective use of AI in project work,
• develop a shared project understanding within their group.

Pedagogical and Ethical Considerations

• The tutorial emphasizes applied learning through structured group work.
• AI is used as a support tool for structuring, reviewing, and reflecting on project content.
• Students remain responsible for all decisions, formulations, and final submissions.
• AI-generated output is treated as a suggestion, not as an authoritative solution.
• Transparent and ethical use of AI is required in accordance with academic guidelines.
• The instructor retains oversight and evaluates the final project assignment.