Generative AI can transform capability assessments into adaptive, finely personalized and intelligent formative tools.
Figure 1 : AI-generated Analysis and Recommendation for Growth Mindset capability assessment
Summative assessments, which judge individuals based on past achievements, often lock people into static categories based on past performance. Integrating generative AI into assessment tools changes the game by offering unmatched objectivity, performance, and adaptability. Shifting to a formative approach, generative AI makes assessments dynamic, focusing on nurturing individual growth and mapping out their future potential. This shift from a backward-looking to a forward-thinking model helps individuals on their learning journey, marking a significant advancement in how we assess and support development.
More Powerful Assessments in Every Respect
Capability assessments, whether it be for soft skills or technical skills, can be rendered more objective, effective and adaptive by leveraging the power of Generative AI.
More objective
Traditional assessment methods like self-assessments and 360-degree evaluations, despite their benefits, are often subjective and prone to biases. Generative AI-driven assessments, however, promise more objectivity by basing evaluations on factual data and actual performance. For example, to measure project management skills, the tool can analyze concrete evidence of successful project management, such as adherence to deadlines, budget management, and team feedback, thereby providing an objective assessment of competence.
Additionally, the gap between self-assessment, peer or manager evaluation, and AI evaluation offers valuable insights. It exposes differences between how we see ourselves, how others see us, and our actual performance, increasing self-awareness and opening avenues for personal development.
More Effective
Validity and reliability are the cornerstones of effective assessments, ensuring that evaluations accurately measure skills and consistently deliver trustworthy results across different contexts. Generative AI enhances validity and reliability in assessments by dynamically adapting to the nuances of each skill and context, and can thus offer tailored assessments that truly reflect an individual's capabilities, significantly improving the trustworthiness and repeatability of the assessment outcomes.
Building on the significant improvements in validity and reliability that generative AI brings to assessments, we see a further evolution in evaluation methods through the adoption of sophisticated techniques. Assessments that are based on multiple measurements and that integrate the powers of generative AI deliver more consistent and unbiased results than traditional methods that rely on a single assessor. This multi-AI strategy, enhanced by Elendi's triangulation methodology, provides a thorough and detailed skills analysis. It guarantees reliable and fair outcomes despite AI's inherent variability by showcasing the powerful combination of AI's analytical capabilities with comprehensive assessment strategies.
Figure 2 : Detailed "Generative AI capability" assessment, with analysis and synthesis by GenAI.
More adaptive
Finally, another major asset of AI-based assessments is its ability to adapt both to the profile of the person being assessed and to the changing requirements of skills.
AI can adjust the difficulty level of questions or propose situational judgment scenarios that match the professional context and experience level of the individual being assessed. Thus, a young manager will be presented with situations suited to their experience, while a senior executive will face more complex and strategic dilemmas.
Given the rapid evolution of skills needed in many sectors, generative AI allows for a dynamic update of assessment criteria to reflect the most recent and relevant competencies. For example, in digital marketing, AI adjusts evaluations to incorporate the latest trends and tools, ensuring that the skills assessed remain in sync with current industry practices.
The Perfect Integration of Assessment and Development
One of the most significant advancements enabled by generative AI is its ability to transform assessments into real springboards for personal and professional development. Thus, assessments are no longer just a benchmark of a level achieved at a specific time but become a full-fledged development tool: this is what we call formative assessments.
Gap to the Next Level
The power of generative AI lies in its ability to finely diagnose the gaps between current skills and those required to reach the next level. Beyond simply identifying shortcomings, generative AI conducts an in-depth analysis to uncover personalized progression pathways, issuing precise and tailored recommendations.
Figure 3 : AI-generated Analysis and Recommendation, for "Generative AI capability" assessment
Training Recommendations
Following the assessment, AI significantly enriches the learning journey by suggesting content specially selected or designed to meet individual needs. Whether identified from a vast existing library or innovatively generated by AI, these resources are perfectly aligned with each learner's development objectives, making the experience even more relevant and engaging.
Continuous Assessments
Integrating continuous assessments throughout the learning process is another fundamental pillar of this approach. These regular evaluations provide detailed and constructive feedback, essential for guiding learners in their progression. This strategy enables the adjustment of individual learning strategies based on achieved outcomes, thus ensuring constant skill improvement.
Impact Measurement
Finally, the ability to conduct evaluations more easily at strategic points in the learning journey offers a precise view of the effectiveness of development programs. This allows L&D managers to measure the actual impact of training initiatives, both at the individual and collective level, and to adjust strategies accordingly to maximize the return on investment.
The Full Power of AI Without the Downsides of a Specific Model
The integration of generative AI into skill assessments particularly raises the question of dependence on specific models.
Model-specific Risks
What problems could this pose? The nature of generative AI is to cheerfully generate content, and it does so with an unwavering confidence that can mistakenly legitimize the content. Yet, every generative AI model (LLM) is built on a number of "editorial choices": for instance, some models may lean towards an Anglo-Saxon, European, or Chinese cultural background; they may incorporate behaviors that are usual, acceptable, or forbidden within certain populations; they may have a more or less pronounced tolerance for insolence or political correctness; they will contain a series of intentional or accidental biases. These biases will necessarily be reflected in the assessments generated. Moreover, these models are constantly evolving, and not necessarily in a transparent way. The risk exists that assessments may be disrupted and flawed as the model changes. Even worse, total dependence on the model could influence assessments without anyone's knowledge.
A Combined Approach
To overcome this challenge, a combined approach is effective: adding proprietary knowledge to a generic language model taps into Generative AI's power while sidestepping biases and limitations of using just one model. This knowledge layer, filled with psychometric expertise like high-quality competency frameworks or precise questions, ensures assessment validity. Thus, it creates a robust foundation for the assessment, protected from the language model's biases. Meanwhile, the language model leverages its capabilities to analyze the content against the benchmarks set by the knowledge layer.
Figure 4 : Elendi's combined approach
Ultimately, blending psychometric expertise with generative AI unlocks the potential for cutting-edge assessments that objective, effective and adaptive. These evaluations become a pivotal part of the individual's journey toward personal and professional growth, seamlessly integrating into their development path.
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