When Benjamin Bloom conducted his study, “The 2 Sigma Problem” in 1984, he evidenced what many had suspected; people learn much more effectively in a personalized environment, such as one-to-one tutoring with an expert, than they do in the classroom or in a group setting.

Ever since this study was published, trying to replicate the positive impacts of one-to-one tutoring has been a mission for many involved in education and organizational development, and a lot of progress has been made in terms of adaptive pathways and tailored content. But the real value in one-to-one tutoring is personalized feedback which has been really hard to replicate at scale — until now!

The new wave of artificial intelligence (AI) models, known as generative AI due to their ability to generate content, whether it’s text, audio or images, opens up a whole new world of possibilities.

These large language models (LLMs) are incredibly clever. For example, you could ask an LLM to write you a bedtime story personalized to your child. It could include their interests; it could be done in the style of their favorite author and it could even create some amazing illustrations to go along with it. But there are also some challenges. Generative AI can’t be entirely trusted to always give quite the right response or even to be factually correct. So, if you’re using it for learning or sharing information, the output needs “to be carefully double-checked as the software seems to generate inaccurate content, based on inaccurately reported sources of ideas,” according to the International Journal of Information Management Volume 71, 102642. And this fact-checking is hard, if not impossible, to do at scale.

Let’s consider how learning leaders can leverage the power of generative AI combined with highly trusted content to create unique, trustable, scalable and effective learning interactions that not only impact the learning experience, but also improve learning outcomes.

For the purpose of this article, we’ll focus on feedback (so the AI initially isn’t generating content or training, but instead analyzing learners’ inputs against a trusted framework).

Why Is Feedback Critical for Learning?

Feedback is a critical component of the learning process, as it provides learners with valuable information about their performance, progress and areas for improvement. Academic research shows that effective feedback can enhance learning outcomes, motivate learners and improve their self-regulation and metacognitive skills.

One study, “The Power of Feedback,” examined the effects of feedback on learning and found that task-specific feedback that focused on how to conduct the task more effectively had one of the largest effect sizes on student achievement. The authors identified three key elements of effective feedback:

• Specificity.
• Clarity.
• Goal orientation.

The study also emphasized the importance of providing timely feedback, as learners tend to benefit more from feedback when it is received immediately after the task is completed.

From the self-regulation model and the research literature on formative assessment, it’s possible to identify some additional principles of good feedback practice. These best practices include providing feedback that:

• Promotes reflection.
• Encourages dialogue.
• Improves self-esteem.
• Provides opportunities to close skills gaps.

Additionally, research has shown that learners who receive feedback that is personalized and aligned with their individual goals and needs tend to be more motivated and engaged in the learning process.

Achieving this type of specific, timely and personalized feedback at scale has long been a challenge since it is so dependent on human interactions.

Delivering Personalized Feedback at Scale

Traditionally, giving personalized, effective feedback has only been achievable on a one-to-one or small-group basis.

In order to deliver feedback at scale, digital solutions have typically used multiple-choice questions or branching scenarios that return pre-determined feedback in response to user selection.

But the use of generative AI will allow managers to build their communication and management skills by practicing the types of difficult conversations they may need to have with people who report to them.

Through generative AI, users will only get feedback in the form of realistic, conversational responses, but they also receive detailed personalized feedback on how they performed — down to the specifics of the exact words they used and how they met certain pre-set criteria.

The future of AI is now, and this level of interaction and feedback will enable learners to practice and build skills in a safe environment.

Here are some best practices to consider when implementing AI for personalized feedback:

• Understand the learning objectives. Ensure that the AI tool or system is aligned with the specific learning objectives and goals of the course. Clearly define the skills and knowledge that learners are expected to acquire, and tailor the feedback to help them achieve those objectives.
• Incremental and constructive feedback. Encourage the AI tool to provide feedback that is incremental and constructive. Instead of merely indicating correct or incorrect answers, the AI tool can guide learners through their thought process, highlight mistakes, and suggest areas for improvement.
• Avoid overreliance on AI. While AI can be a valuable tool, it should not replace human interaction and feedback entirely. A balanced approach that combines AI-driven feedback with human interaction can offer the best of both worlds.

By leveraging the best practices outlined above, you can begin to leverage AI to deliver personalized feedback at scale.