With its ability to quickly summarise survey answers, recognise trends and identify areas for improvement, AI can revolutionise training feedback analysis, writes Chris Wigglesworth
Have you ever looked at the pile of feedback survey responses and wondered how you’ll find the time to pick out the relevant points that you can act on to improve your training programmes? Well, the answer lies in the power of artificial intelligence (AI) and its ability to analyse large volumes of content.
AI allows you to be very specific about the insights you want to extract – anything from improvement suggestions for the booking process to learning outcomes
Until recently, training providers were constrained by the amount of unstructured feedback they could analyse, simply due to the time-consuming nature of free text responses and inability to distil this information into manageable data. However, advancements in large language models (LLMs) and the widespread availability of generative AI tools such as ChatGPT have dramatically changed that.
Text responses provide some of the most detailed and nuanced feedback. Unlike multiple-choice or score-based questions, open-ended questions invite learners to share thoughts on areas of the course which may not feature anywhere else in feedback surveys. Generative AI has the power to significantly reduce the time it takes to analyse this qualitative data by summarising survey answers, recognising trends and identifying areas for improvement.
As organisations strive to optimise their training processes, the integration of AI has emerged as a gamechanger. In this article, we’ll delve into the crucial role AI plays in revolutionising training feedback analysis and the impact it has on optimising learning programmes.
The benefits of using AI to summarise training feedback
Speed and efficiency
One of the biggest benefits of using AI for training evaluation analysis is that it can quickly analyse large volumes of training feedback data and generate concise summaries much faster than manual methods. This saves time and resources, allowing trainers and administrators to focus on implementing improvements rather than spending extensive time on the analysis.
Pattern recognition
AI can identify patterns and trends within the training feedback that may not be immediately apparent to human reviewers. This can help in uncovering common issues, strengths,or areas for improvement across various training sessions or participants.
Real-time feedback monitoring
One of the significant advantages of AI in training feedback analysis is its ability to provide real-time insights. Traditional feedback analysis methods often involve a time lag between data collection and interpretation. AI systems, however, can analyse feedback as it is received, allowing organisations to promptly address any issues or concerns raised by participants. This real-time monitoring enhances the agility of training programmes, enabling quick adjustments to optimise the learning experience.
Feedback prioritisation
AI can prioritise feedback based on its relevance and impact, helping trainers and administrators address critical issues first. This ensures that the most important concerns are tackled promptly, leading to more effective training outcomes.
Data-driven decision making
Summarised feedback generated by AI provides a data-driven foundation for decision making. This can help organisations make informed choices about training content, methods and overall strategies, leading to better outcomes and participant satisfaction.
Scalability
AI solutions can scale easily to handle large datasets, making them suitable for organisations with extensive training programmes or numerous participants. This makes it an ideal tool for organisations that receive high volumes of learner feedback or operate on a global scale.
Enhanced objectivity
AI can help maintain objectivity in feedback analysis, reducing the influence of personal biases. This ensures that feedback is evaluated based on predefined criteria and standards, reducing the potential for errors in interpretation.
Continuous improvement
By summarising feedback consistently and efficiently, AI can contribute to a continuous improvement cycle. Trainers can easily track changes over time, assess the effectiveness of implemented improvements, and make data-driven decisions to enhance future training programmes.
Crafting effective prompts for training feedback analysis
The prompts you use will dramatically influence the quality of the output you get from AI-generated reports. It is important to experiment with your input instructions and fine-tune them to generate a summary report that meets your requirements.
Depending on the goal of your analysis, you can use different types of instruction templates to generate different insights from the same data:
Overall course summary:
- Summarise feedback related to the course structure and organisation.
- Provide a brief summary of comments regarding the effectiveness of the course content.
- Extract insights on the quality and relevance of the course materials.
- Provide suggestions for improvement.
Instructor evaluation:
- Summarise feedback relating to the performance of a particular instructor.
Segmentation:
- Segment feedback based on participant demographics or previous experience.
- Segment feedback into positive and negative sentiments for each parameter (instructor, programme, etc.).
- Segment feedback to assess the improvement or decline in each programme over an extended period.
These are just a few examples of prompts to use when analysing text responses. You can be very specific about how you want the output to be presented back, for example, defining how you want your report to be structured and how long it should be. In short, the quality of AI-generated output largely depends on the quality of the input instructions.
Getting down to specifics
As well as giving a general overview of what’s working and what’s not, AI has the power to dig down into detail and summarise responses at a question level. It gives you the ability to be very specific about the insights you want to extract, which could be anything from improvement suggestions for the booking process to learning outcomes.
Here are some examples of feedback survey questions where AI-generated comment analysis can offer real insight:
- Will you be able to apply what you’ve learnt on this programme? If yes, what specifically will you be able to apply in your role?
- What one thing could have made today’s workshop better?
- What will you do differently as a result of this course?
- Are there still learning gaps that you need more training on?
Breaking the language barrier
AI can play a significant role in summarising training feedback in multiple languages by using multilingual natural language processing (NLP) algorithms to analyse the sentiment, key phrases, and context of feedback, irrespective of the language it is written in.
AI-powered translation can be integrated to automatically translate feedback from different languages into a common language, helping to standardise the information and allowing for a more uniform analysis.
Advanced NLP models can understand the context of the feedback, taking into account cultural nuances and language-specific expressions. This ensures that the summarised feedback accurately captures the intended meaning, providing valuable insights into the effectiveness of training programmes globally.
Continuous improvement through predictive analytics
AI excels in predictive analytics, and this capability can be harnessed to forecast future training needs. By analysing historical feedback data, AI systems can identify trends and predict potential challenges or gaps in training programmes. This foresight empowers organisations to proactively adjust their training strategies, ensuring continuous improvement and alignment with evolving skill requirements.
Personalised learning paths
AI-driven feedback analysis goes beyond just collecting data; it also facilitates the creation of personalised learning paths. By identifying patterns in feedback data, AI algorithms can pinpoint specific areas where individuals or groups may need additional training or support. This enables organisations to tailor their training programmes to meet the unique needs of each employee, fostering a more efficient and targeted learning experience.
Conclusion
The incorporation of AI into training feedback analysis marks a transformative shift, fundamentally altering how organisations optimise their training programmes. Carrying out effective training evaluation provides a golden opportunity to get insights that can directly impact training quality. And AI allows you to dig out this buried treasure while minimising the amount of human intervention needed to analyse data and identify subsequent actions.
Through effective prompts, AI-generated reports can cater to specific analytical goals, generate improvement suggestions, and identify patterns over time. The synergy between AI and training evaluation promises to redefine the landscape, ushering in a more adaptive and tailored approach to training and development.
Chris Wigglesworth is Managing Director of Coursecheck
