Artificial Intelligence

Applications of AI in Learning and Development (Training Needs Analysis) Part I

Learning and Development

Who should read this blog? You should read this blog If you are a part of the Learning and Development team or an Operations or a Technology Leader wanting to understand the different possible applications of AI across your Learning and Development function.

This blog is going to focus on AI in the first step of the process that is: Training / Learning Needs Analysis.

2020 is the year where L&D is going to have a critical role to play in the Business as they will drive significant Business Outcomes. Why so? Because the digitally transformed world we live in today, has a rapidly changing skill map. The Average skill shelf life has decreased to about 5 years and the need to be agile to have new skills has proportionately increased.

Let us look at the top 10 Soft Skills required in 2020 and compare it with 2015. Significant change, wouldn’t you say? Creativity and critical thinking have moved up, emotional intelligence and Cognitive Flexibility are new skills that have been added. Cognitive Flexibility in many ways is like parallel processing, it is the mental ability to switch between thinking about two different concepts, and to think about multiple concepts simultaneously.

2020
Source: Top 10 Skills in 2015 & 2020 (Source: World Economic Forum, 2016 https://www.weforum.org/agenda/2016/01/the-10-skills-you-need-to-thrive-in-thefourth-industrial-revolution/

The top 10 hard skills are:

  1. Cloud Computing
  2. Artificial intelligence
  3. Artificial Reasoning
  4. People Management
  5. UX Design
  6. Mobile Application
  7. Video Production
  8. Sales Leadership
  9. Translation
  10. Audio Production
  11. Natural Language Processing

How many of these would you have recognized back in 2015? Barely any.

The global training spends in 2019 was $370.3 BN an increase of ~40% in a decade. (https://trainingindustry.com/wiki/outsourcing/size-of-training-industry/). To hire and retain good talent organizations will literally have to become learning campuses and ensure they are owning lifetime learnability of their employees.

The first and most significant step in establishing a learning campus is to assess the learning needs. Also called training needs analysis, unfortunately this one step that is the most rushed and usually not completed.

Training needs analysis is conducted at three levels:

Training

However, most organizations manage to complete the 1st 2 levels only. Individual level done manually, is a highly time consuming and intensive process and therefore is usually left incomplete or done in parts.

The data and information needed to assess at an Organizational level is Business Goals, Employee & Skills Inventory, Customer Satisfaction Data and Organization Culture. Usually easily available.

Goals

Next you look at the Operational Level which is more detailed but again available with each of the operation & functional HR heads. Data like Job description & specifications, Work performance standards and metrics.

Job Description

The 3rd Level is the Individual level which is most challenging. As you need to get into a lot of data mining across the organization. Performance appraisal of an employee, to skill assessments, Interview questionnaires, work samples all this data needs to be collected, looked, and analyzed together to tell you the complete story.

Data From Any Where

Imagine even for an organization of 100 it is a lot of data and if we are talking of 100,000 it is a mammoth task.

Artificial Intelligence can be of great help here. As its most significant accomplishment is that it can help you process large amounts of data at scale yet personalized at significantly lower costs.

A key component of Learning Needs Assessment is assessing the skill gaps. And that is where one of the oldest applications of AI Adaptive Testing can be of significant help. This technique dates back to 1970s’. Companies like Pearson have created several adaptive tests to be able to provide personalized coaching to students.

The corporate sector is a late adopter however we have platforms like IRIS from Plural Sight which combines adaptive testing theory with machine learning to create statistical models for skill levels. To stay current, Iris adapts and collects data on trending technologies in terms of which are popular, and what is getting obsolete.

Adaptive tests when combined with machine learning can maximize Learning Need Assessment measurement efficiency. The Assessment will be more accurate and precise too. Since Adaptive tests adapt to the test taker’s ability the assessment times are shorter.

Adaptive assessments work by leveraging Item Response Theory (IRT). IRT focuses on the difficulty of each individual item as in question, rather than the overall assessment. As users answer questions, the adaptive assessment infers their likely skill level with increasing probability based on the difficulty of each question answered.

Because you calculate probabilities of the employee’s capability level It becomes unnecessary, then, to serve up all assessment questions, enabling short-form assessments to replace traditional long-form tests. So, in a way based on the questions answered you already know the questions you don’t need to ask. The probability of a correct response is determined by the employee’s ability and the difficulty level of the question.

If you were to plot the probability curve it would look like this. As the ability increases the probability of answering the question goes up. For example, at Ability 5 the probability is .98 and at 0 its .5.

Curve

Adaptive learning can be used both for Hard and Soft Skills. We saw an example of hard skills with IRIS where they are using it for assessing the technical skills of their employees. For Soft skills you could create a host of psychometric tests across the following areas and run it on an adaptive learning platform:

Faces
  • Personality Tests: Personality tests are a method of assessing human personality constructs. They are based on collecting personal information about people they draw inferences about how an individual think, feels and behaves. That in turn can predict persons performance at work when it comes to interpersonal skills, management style, motivation levels, ability to handle crisis and performance under pressure
  • Cognitive Tests: Cognitive tests are all about measuring competence and intellectual capabilities. They’re able to fairly accurately predict performance at work as they measure a person’s thinking abilities such as perception, reasoning, memory, verbal, and problem-solving ability. They also test for the ability to solve problems when learning new job skills or tackling workplace issues.
  • What about soft skills or acquired skills? can you think of ways we could use adaptive assessments to measure these. Challenging but not impossible. If you could find ways to marry on the job performance data to personality and cognitive traits of an individual, you could get a good measure of progress made on soft and or acquired skills.

What are the Benefits of Adaptive Assessments?

Highly Accurate: Adaptive testing allows you to identify an employees’ true level of ability faster and more accurately than with other types of assessments. By starting with a question of average difficulty, and then asking harder questions when they get it right and easier questions when they get it wrong, each additional question homes in on a narrower and narrower range of ability. Until the test has zeroed in on an employee’s exact level of ability and you get to the specific skill gap that needs to be addressed.

Positive Employee Experience Since each examinee is challenged appropriately during an adaptive test, the overall experience is more positive than a traditional assessment. Low performers are not discouraged or intimidated, while high performers won’t get bored and even enjoy receiving more difficult items. A better test-taking experience encourages employees to try harder than they might with a conventional test. Low performers end up feeling more comfortable and less judged and high performers will feel challenged and more motivated.

Innovative and Leading Edge: Adaptive testing leverages artificial intelligence, as candidate responses to each test question inform the next item that appears in the test.  This dynamic and interactive back and forth between the candidate and the test quickly homes in on the candidate’s true level of ability, more so than any other type of assessment available today. They can use various forms of audio, visual, and video content in their adaptive tests.

Adaptive learning represents a paradigm shift—from the conventional model (an instructor-centric, passive learning experience) to an intelligent one (a learner-centric, interactive, active learning experience). In the adaptive model, each employee is paired with a virtual “coach.” It’s a concept that can be scaled to millions of employees at a fraction of the cost of human coaches.

Do you want to put an adaptive learning platform to work? Do share with us your thoughts in the comment section below.

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