Greetings colleagues,

Thank you, Muriel, for bringing up this topic, and I appreciate all the contributors. I believe that AI holds promising features for evaluators, and it's crucial for us to be aware of them. Personally, the prospect of conducting fast and interactive quantitative analysis without the need for expertise in code-based software (e.g., R or Python) would be a game-changer for professionals like myself with backgrounds in human sciences.

Additionally, the capability of summarizing extensive raw texts, such as interviews or focus group discussion transcripts, and facilitating accurate analysis of key points, has the potential to save a significant amount of time. However, it's essential to highlight that the evaluator's experience, prior knowledge of the field, insights from stakeholders, and a sense of the evaluation's purpose will continue to be crucial and valued.

Moreover, ethical dilemmas and decisions on how to present results won't be solved by AI, no matter how powerful it becomes.

I would love to see examples of AI used in both quantitative and qualitative approaches.