The convergence of AI and learning has redefined corporate training methodologies, shifting away from antiquated one-size-fits-all approaches towards personalized, scalable, and efficient learning ecosystems. In an exclusive interview, People Matters spoke with Asma A Shaikh, Co-Founder & Director of enthral.ai, which is a 14-year old L&D company specialising in learn-tech platforms that are used by over 250 enterprises and brands including Aarti Industries, Piramal, Bosch, Microsoft (US), Sembcorp (Singapore).
During the course of this insightful discussion, we delved into the transformative role AI plays in shaping modern corporate training and upskilling through a learning management system (LMS) and learning experience platform (LXP).
As a pioneering force in integrating technology and learning, Shaikh offers invaluable insights into the dynamic landscape of AI-powered learning technology platforms, its potential, challenges, and the promising future it holds for learners worldwide. Join us as we uncover the nuanced intersections between AI and learning, exploring the innovative strides and possibilities in this ever-evolving field.
The world of learning and development has undergone a monumental change, with artificial intelligence changing our approach to skilling, upskilling, and reskilling. What kind of evolution have you noticed in this field in the last, say, five years?
The revolution brought about by AI has profoundly transformed corporate learning and development (L&D) by infusing efficiency, personalisation, and innovation into training programs. Unlike the traditional one-size-fits-all approaches, AI has enabled the customisation of learning paths and content to align with individual learning styles, all while maintaining scalability. Analytics, meanwhile, furnish invaluable insights into employee performance, engagement levels, and skill gaps, empowering organisations to strategically invest in upskilling and reskilling initiatives targeting specific competency needs.
The automation introduced by AI not only streamlines administrative processes but also frees up critical time for L&D professionals, allowing them to concentrate on the more strategic aspects of their work. AI-driven chatbots and virtual assistants expertly handle routine queries, enhancing the overall learning experience.
AI contributes to a continuous learning culture by delivering ongoing feedback, providing nudges for learning reinforcement, and offering personalised recommendations for skill development. This integration ensures that learning becomes a perpetual component of the employee experience.
In the past, remote training options were limited, and digital learning lacked sophistication. However, with the advent of AI, virtual classrooms, collaboration tools, and remote learning platforms have become necessary for modern learning.
As newer skills gain mainstream importance and acceptance at a quick pace, skill-based learning is now critical. How can AI be leveraged to create personalised learning journeys to bridge skill gaps?
With the accelerating pace of skills evolution in both traditional and emerging roles across all sectors, the need for proactive and innovative skill-building and talent management has become a pressing concern. Addressing this requires a talent development function that is rapidly evolving into a more strategic entity, securing a prominent seat at the decision-making table. In the face of technology advancing at an unprecedented rate, the conventional top-down model of enterprise learning is no longer sufficient.
The need of the hour is a unique approach that empowers employees to enhance their skills precisely when needed. One way is using robust learning platforms that identify the necessary skills for specific roles based on global industry standards, conduct thorough skill assessments by analysing existing employee data (including performance records, project outcomes, and training history), and then tailor individualised learning plans.
These plans can cover a blend of online courses, workshops, mentorship programs, and on-the-job experiences, ensuring a comprehensive approach to skill development. As employees progress, the system adapts to their strengths and weaknesses, delivering a customised learning experience.
AI has the potential to provide real-time feedback and insights on employee progress. How do you use AI-driven analytics to measure the effectiveness of your training and upskilling initiatives, and how does this data inform decision-making?
Conventional learning approaches are falling short of meeting the evolving needs of modern organisations and learners. Obsolete, one-size-fits-all training methods often result in disengaged employees, low knowledge retention, and limited business impact.
A great example of how we use AI analytics is our Learning Experience Platform (LXP), where AI scrutinises data related to learner engagement, including metrics like time spent on courses, completion rates, and interactions with various learning materials. This analysis provides valuable insights through engagement analytics, pinpointing which courses or modules garner the most popularity — indicating high-interest topics and identifying areas where learners may be disengaged.
This information empowers organisations to re-evaluate their content and thereby refine or expand certain courses accordingly. They can also adjust content delivery methods and address potential gaps in engagement.
Additionally, our AI-based proctoring tool contributes greatly towards creating a secure and controlled environment for assessments measuring learning impact. This tool enables our frontline customer base to assess employee learning on the fly, offering tailored feedback and certification.
As AI continues to evolve, what ethical considerations and privacy safeguards does your organisation prioritise in the context of AI-driven learning, and how do you maintain transparency and trust with the end user?
By giving priority to the considerations outlined below, we ensure that our learning initiatives adhere to ethical standards and privacy safeguards. We consistently review and update these practices to align with evolving ethical standards.
Data Minimization: Collect and retain only the data necessary for the AI-driven learning system to function effectively. Minimise the amount of personally identifiable information (PII) processed.
Anonymisation and aggregation: Ensure that individual user data is anonymised or aggregated whenever possible to protect user privacy.
Encryption: Implement robust encryption protocols to secure data during transmission and storage, preventing unauthorised access.
Access controls: Restrict access to AI-generated data to authorised personnel and ensure that proper access controls are in place.
Data protection laws: Adhere to relevant data protection regulations, such as GDPR, ISO 27001, or other local and international privacy laws that apply to the collection and processing of user data.
Regularly reviewing and updating these practices is crucial to adapt to evolving ethical standards and user expectations.
Looking ahead, what do you see as the most exciting developments or trends in the intersection of AI and learning for training, upskilling, and workforce development, and how is Enthral preparing to stay at the forefront of this evolving landscape?
Our guiding philosophy revolves around being a strategic learning partner that adds significant value to an organisation’s learning ecosystem. The primary focus is fostering the adoption and practical application of learning, ensuring seamless knowledge transfer. While we advocate for the union of technology and learning to create a sophisticated and effective learning platform, our future roadmap comprises the following:
Blockchain for credentialing and learning records:
We plan to increase the use of blockchain technology to manage credentials, certifications, and learning records securely and transparently. We will integrate blockchain to create immutable, verifiable records of skills and achievements, offering a decentralised and secure way to manage lifelong learning credentials.
Personalised AI coaches and mentors:
We will feature more sophisticated AI coaching systems that emulate human-like interactions, offering tailored advice and adapting to individual learning preferences. This could be possible through AI-driven virtual coaches and mentors who provide personalised guidance, feedback, and support throughout the learning journey.
AI-powered learning analytics ecosystems:
This includes more sophisticated metrics to measure learning outcomes, collaboration, and real-world application of skills. AI analyses performance data from videos submitted by learners to evaluate understanding and application of knowledge. Insights into specific areas of strength and weakness for individual learners and the overall cohort help assess the effectiveness of the training content.
User Sentiment Analysis is possible by analysing facial expressions, where the tool can gauge user emotions and engagement levels. Positive expressions may indicate understanding and interest, while negative expressions may suggest confusion or disinterest.
Analysis of user interactions, including pauses, rewinds, and fast-forwards, provides insights into attention spans and areas that may require additional clarification. Analysis of broader body movements helps understand the user’s level of engagement, energy, or comfort with the presented content. The integration of these advanced features in our platform will contribute to a comprehensive and dynamic learning experience, fostering engagement and knowledge transfer.