Given the demand for responsible and trustworthy AI for education, this study evaluates symbolic, sub-symbolic, and neural-symbolic AI (NSAI) in terms of generalizability and interpretability. Our extensive experiments on balanced and imbalanced self-regulated learning datasets of Estonian primary school students predicting 7th-grade mathematics national test performance showed that symbolic and sub-symbolic methods performed well on balanced data but struggled to identify low performers in imbalanced datasets. Interestingly, symbolic and sub-symbolic methods emphasized different factors in their decision-making: symbolic approaches primarily relied on cognitive and motivational factors, while sub-symbolic methods focused more on cognitive aspects, learnt knowledge, and the demographic variable of gender -- yet both largely overlooked metacognitive factors. The NSAI method, on the other hand, showed advantages by: (i) being more generalizable across both classes -- even in imbalanced datasets -- as its symbolic knowledge component compensated for the underrepresented class; and (ii) relying on a more integrated set of factors in its decision-making, including motivation, (meta)cognition, and learnt knowledge, thus offering a comprehensive and theoretically grounded interpretability framework. These contrasting findings highlight the need for a holistic comparison of AI methods before drawing conclusions based solely on predictive performance. They also underscore the potential of hybrid, human-centred NSAI methods to address the limitations of other AI families and move us closer to responsible AI for education. Specifically, by enabling stakeholders to contribute to AI design, NSAI aligns learned patterns with theoretical constructs, incorporates factors like motivation and metacognition, and strengthens the trustworthiness and responsibility of educational data mining.

Adoption has more than doubled since 2017, though the proportion of organizations using AI 1 1. In the survey, we defined AI as the ability of a machine to perform cognitive functions that we associate with human minds (for example, natural-language understanding and generation) and to perform physical tasks using cognitive functions (for example, physical robotics, autonomous driving, and manufacturing work). A set of companies seeing the highest financial returns from AI continue to pull ahead of competitors. The results show these leaders making larger investments in AI, engaging in increasingly advanced practices known to enable scale and faster AI development, and showing signs of faring better in the tight market for AI talent. On talent, for the first time, we looked closely at AI hiring and upskilling.

This report reveals that households across the country are still grappling with the financial ramifications of COVID-19, and women are among the hardest hit. And, while alarmingly few employees seek out financial advice, those who do fared better during the pandemic and are more confident in their ability to reach their financial goals. Download this report and see new emerging trends and learn how to take a more proactive role in your employees' financial wellness.

Expert Opinion Using machine learning to keep your high performers Machine learning leads the way to win-win situations: success for your organization and happiness for your colleagues. Machine learning leads the way to win-win situations: success for your organization and happiness for your colleagues.

For the past few years, the McKinsey Global Institute has been conducting a yearly survey to assess the state of AI adoption. Its 2017 survey of over 3,000 AI-aware executive found that outside the technology sector, AI adoption was at an early, often experimental stage. Only 20% of respondents used any AI-related technology in a core part to their business. A common theme throughout the report was that the same players who were leaders in the earlier waves of digitization and analytics were the early leaders in the AI wave. The McKinsey 2018 survey garnered responses from over 2,000 participants from a wide range of company sizes across 10 industry sectors. Overall, the 2018 survey found that while the business world had begun to adopt AI, few companies had in place the foundational building blocks that would help them to generate value from AI at scale.

CPOs and their teams can take a page from this book and set themselves up for success going forward. Here's what they can consider: Focus on relationships and influence across functions and supply markets: Our research found that high performers and agility masters perform better on a higher "quality of [stakeholder] influence" rather than just the "quantity of sourcing-centric [spend] influence." Procurement organizations need to think of managed service providers and ecosystem partners as their extended enterprise and put stakeholder/customer management at the center of their strategy. They can build collaborative muscle by flipping their linear sourcing-centric approach to third-party/partner management and developing a holistic supplier management approach. Define a truly balanced scorecard: CPOs have generally done well in terms of achieving their savings targets.

Employee AI 360: Integrate employee data from multiple source systems using pre-built data connectors and provide a single view of an employee. Employee data gets enhanced by external data we provide to give deeper insights; we apply machine learning techniques to give you a real AI 360 degree view of an employee. Analyze Employee Experience: Text Mining and Machine Learning techniques are used to analyze the text data provided by employees in surveys, performance appraisals, etc., to better understand employee experience. Analyze for Unconscious Bias: Ensure that analytics models and predictors are not inherently biased. Talent Analytics: Using ML and behavioral analytics identify the right candidate for a particular job description based on Skill Score, Success Profile Score, and Propensity Score to accept an offer.

In the latest McKinsey Global Survey on AI we noted a significant year-over-year jump in companies using AI across multiple areas of the business. And while most survey respondents said their companies have gained value from AI, some are attaining greater scale, revenue increases, and cost savings than the rest. Based on our research and experience, this is no accident; how companies build their business strategy, what foundations they put in place, and how they tackle AI adoption in the workplace can all impact their potential for transformation. Many companies that have spent years developing AI technologies are facing the stark reality that successfully scaling AI requires more than just deploying AI technology. We find that those companies finding more success in scaling efforts are more likely than others to apply a core set of practices.

In this episode of the McKinsey on AI podcast miniseries, McKinsey's David DeLallo speaks with McKinsey Global Institute partner Michael Chui and associate partner Bryce Hall about the latest trends in business adoption of artificial intelligence (AI). They discuss where the technology is being used most across industries, companies, and business functions; the keys to getting impact from AI investments; and what lies ahead. There's no shortage of predictions about how it could fundamentally change the way we live and work. Over the past few years, companies around the world have been figuring out exactly how AI technologies can improve their performance in a number of areas across their business. But is AI actually delivering significant results? Moreover, what can we expect to see as we move into a new decade of AI use and development? To answer some of these questions today, I'm joined by Michael Chui, a McKinsey partner with the McKinsey Global Institute, who is based in our San Francisco office, and associate partner Bryce Hall from our Washington, DC, office.

The latest McKinsey Global Survey released today found that artificial intelligence is having a positive impact on business outcomes, with 63% of respondents reporting an increase in revenue after adoption of the technology. However, only 30% of companies apply AI to multiple business units, up from 21% last year. Still, overall AI adoption is on the rise -- in standard business practices it's up near 25%, according to the online survey of 2,360 business leaders from a range of industries and global regions. The report also found a number of striking differences between companies deploying several AI systems to business operations and those that are not. Companies that are considered high performers or AI power users by the study on average apply the technology to 11 use cases compared to three use cases at other companies using machine intelligence. The high performers were also more likely to report revenue increases from AI applications of over 10%.