5 Challenges of the Learning Objective Process
We wanted to not only enable self-directed learning, but also make it measurable, and so we developed our learning objective agent.
Challenge: Supporting self-directed Learning with over 200 Employees
Self-directed learning is now a key success factor for organisations that want to develop in a dynamic environment. But how can the quality of individual learning objectives be ensured when many employees are working on their development at the same time?
At BREDEX, we place a strong emphasis on self-directed learning. After consultation, employees choose topics that are relevant to their role development and skills acquisition. A central element of this approach is the formulation of specific learning objectives. After all, only those who know what they want to learn can subsequently recognise whether their learning has been successful.
In practice, however, a challenge quickly became apparent: supporting more than 200 employees in developing their individual learning goals was hardly feasible in terms of personnel. The process required a great deal of didactic support and personal discussions in order to formulate specific, realistic and verifiable goals.
The central question was: How can individual learning support be scaled without compromising the quality of the learning goals or the self-direction of the learners?
The five Challenges of the Learning Objective Process
- Lack of time for individual support
- Varying didactic quality of learning objectives
- Balance between self-management and support
- Lack of scalability as the number of employees grows
- Low transparency and measurability of learning success
These points made it clear that we needed a solution that was didactically sound, low-threshold and scalable.
Solution: The BREDEX Learning Objective Agent helps Employees to formulate clear Learning Objectives
To solve this challenge, we have developed an AI-based learning objective agent. The agent is integrated into our internal AI tool, allowing employees to start creating their learning objectives directly in a chat window without additional preliminary information or complex input masks.
The AI conducts a structured, dialogue-oriented conversation with users. It first asks about their individual learning needs, clarifies these in the exchange, and formulates one or more tailored, verifiable learning objectives. Upon request, the agent also suggests suitable learning strategies or activities to achieve these goals, such as practical exercises, exchange formats, or digital learning resources.
What has changed as a result of the AI Learning Objective Agent
After the development phase and the initial prototype release, the learning objective agent was introduced. Feedback from employees has been positive. The agent helps to organize thoughts and formulate learning plans in such a way that they are concrete and feasible. From the perspective of the Learning & Development team, the solution also offers clear added value: It creates scalability without losing the individual relevance of the learning goals and at the same time provides a basis for making learning more transparent and measurable in the future.
The learning objective agent does not replace learning support, but rather complements it in a meaningful way. It relieves the didactic team, promotes personal responsibility among learners, and creates a basis for data-driven continuing education development.
Conclusion: AI and Self-Regulation as the Future of Learning
With its learning goal agent, BREDEX has taken an innovative step towards AI-supported, self-directed learning. It does not replace learning support, but rather complements it in a meaningful way: it relieves the burden on the learning and development team and team leaders, promotes personal responsibility among learners and creates a basis for data-driven continuing education development.
Autorin
Lea Lachmann
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