LOOKING TO IMPLEMENT DATA ANALYTICS AND AI TO TRANSFORM LEARNING? CHECK FOR THESE POTENTIAL MINEFIELDS AND BEST PRACTICES

NAVIGATING THE DATA PRIVACY MINEFIELD

As the adage goes, with great power comes great responsibility. And AI’s immense capabilities do not come without their share of concerns.

1. Data privacy sits at the top of this list. Consider the widely discussed tools like ChatGPT, Bard, or Claude. Leveraging their capabilities requires sharing company data over cloud platforms, which do not always offer transparent insights into data protection mechanisms. Many platforms are already building a private layer on top of ChatGPT to protect company data.

2. Beyond security, Data Analytics and AI also sparks conversations around ethical usage and inherent biases. For a deeper dive into this topic, watch ISPI LIVE webinars such as “Best Practices and Biases of AI” from Veronika Shestakova, and “The Rise of AI in L&D” from Stella Lee.

3. The widespread use of generative AI remains challenged by intellectual property rights and proper citations.

PERSONALIZED LEARNING THROUGH AI: A CLOSER LOOK

Let’s explore some of these cutting-edge applications of AI. Personalized Learning represents one of the most significant advancements in the realm of learning and Performance Improvement. Many platforms already embraced data-driven solutions to personalize the learning experience for each user. Here are some significant ones that have been changing the game:

• Duolingo uses AI to personalize the learning experience for each user by tracking their progress and adjusting the difficulty of the lessons accordingly.

• Apps like Knewton, Realizeit, or EdApp assess each learner’s strengths and weaknesses, and then tailor the learning content accordingly.

• Cognitex uses AI to assess each learner’s learning needs and preferences, and then tailors the educational content accordingly.

There are great examples. But all require repurposing content, configuring a new platform, paying per-user licenses, and integrating the new systems in the existing learning landscape

A SIMPLE BUT EFFECTIVE ADAPTIVE LEARNING CASE STUDY

Alternative solutions to stand-alone flashy platforms exist. They are certainly less sophisticated but can be as effective. A couple of years ago, a team of innovators and technologists at VPS Learning (vpslearning.com) piloted the implementation of personalized learning for a US-based technology company.

The challenge was to improve the performance of a regional team of 250 field technicians responsible for servicing devices installed in banks and shopping centers. The measure of individual performance was the ability to fix the device right the first time. We first applied VPS’ analysis and design methodology to optimize the learning path for efficiency and effectiveness. Then, VPS implemented targeted learning interventions by using performance data and deploying the right learning modules for the right learners within the existing Learning Management System.

Within 12 months, this helped reduce the number of low performers from 62% to 4%. The program has garnered multiple industry recognitions, including the Chief Learning Officer Gold “LearningElite” award, the HRO Today “Partnership in L&D Excellence” award, and the Brandon Hall “Excellence in Learning” awards.

HOW CAN WE ALL MAKE A DIFFERENCE AND SHAPE THE LEARNING AND PERFORMANCE IMPROVEMENT INDUSTRY?

1. Start by acquiring new skills. Learn prompt engineering and become proficient in handling learning and performance data. Test GPT-4’s ability to perform advanced data analysis. Understand how to evaluate and closely examine AI algorithms. Lastly, gain a solid understanding of AI methodologies and their related risks. This knowledge will help in setting up guidelines for ethical and responsible AI use.

2. Stay updated with the latest advancements in the industry. Explore Kambria’s AI Innovation Lounge, a platform dedicated to fusing AI and Learning & Development best practices. It provides complimentary podcasts, real-world use cases, and many AI prompt examples.

3. Dive into the world of AI by experimenting with platforms such as ChatGPT, Bard, and Claude. These tools offer hands-on experience, allowing you to understand their potential and nuances. Trying them out personally can give you a better grasp of their capabilities and how they might “assist” you and your teams.

4. Ask your learning service providers to present their case studies and latest innovation. It is critical to have a solid business case and solid plans to protect your data before adopting AI or implementing a new, shiny platform. Any transition will consume resources, the business case for change should provide evidence that the new approach will bring measurable improvements to your learners, help them accomplish more and thereby positively influence business results.

5. Join a professional association like ISPI to expand your professional network, participate in monthly webinars or the annual conference, and access evidence-based tools and strategies.

6. Contribute to the discussion on platforms like AI for Performance Improvement (AI4PI) on https://www.ai4pi.net. If you have best practices to share, apply to join ISPI’s AI4PI innovator circle.

7. Publish blogs and articles, shape the industry. There are many professional journals like the Performance Improvement Journal to showcase your best practices, or the Performance Improvement Quarterly if you want to publish peer-reviewed research outcome.

SETTING SAIL INTO THE AI-ASSISTED FUTURE OF LEARNING AND PERFORMANCE IMPROVEMENT

The transformative power of Data Analytics and AI is undeniable. While these technologies open doors to unprecedented personalization and efficiency, Learning and Performance Improvement professionals must be equally vigilant about the challenges they present, particularly in the realms of data privacy, ethical considerations, and implementation costs.

Harnessing AI’s capabilities is not just about leveraging tools like GPT, Bard, or Claude; it is about embedding a culture of continuous “assisted” learning, evidence-based decision-making, and fostering collaboration within Learning and Performance Improvement professional organizations like the International Society for Performance Improvement.

As pioneers in this new landscape, our collective responsibility is to ensure that Data Analytics and AI serve as enablers, enhancing the potential of our learners and driving measurable performance improvements. By remaining attuned to industry advancements, rigorously evaluating the impact of new initiatives, and championing ethical and responsible usage, we are not just charting new frontiers—we are defining them.

The journey ahead promises to be as challenging as it is exciting. Let’s navigate it with foresight, adaptability, and a commitment to excellence. This is how we will truly make a difference in our industry.