AI In Education: Machine Learning Futures

By Zainab Cheema 

When grocery shopping over Christmas, I knew that my final purchase wouldn’t be chocolate. It would be the Harvard Business Review special issue How AI is Changing Work, decked on the checkout aisle. This special issue inspired me to write a blog post on the topic, reviewing it from the standpoint of the growing role of Artificial Intelligence in Education. So, here is the question of the day—what are the major trends shaping AI’s widescale incorporation in work, and how do we apply these to the Ed industry?  

Sci-fi and Netflix dystopian TV allow us to continue to broadcast our discomfort with AI’s ethical implications, even as we grow increasingly reliant upon it. But what exactly is AI? As Erik Brynjolfsson and Andrew McAfee put it, AI, or more particularly Machine Learning (ML), “is the machine’s ability to keep improving its performance without humans having to explain exactly how to accomplish all the tasks it’s given” (22). The transformational impact of AI can already be seen in Netflix algorithms predicting your next favorite show to Alexa’s Akinator playing games with you and your family. AI is being leveraged in manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment (22). Javeria Salman reports that “According to Stanford University’s 2021 AI Index, more than $40 billion was invested in all AI startups in 2020.” This trend is only set to grow.

The question is, can (and to what extent) AI be employed in Education? AI is taking the medal in fields and applications that are heavily based in perception and cognition. In other words, AI is transforming applications such as voice recognition (increasingly being used in dictation); facial perception (now used in social media and surveillance systems); large scale data crunching, which feeds into customer service predictions. But what is it that AI cannot do? Communication. Collaboration. Storytelling. Namely, the three fundamental skills that Higher Ed (and really, Education as a whole) is based on.

How AI is Changing Work carries the easy, breezy tone of big business optimism that the machine and human worlds can be optimally synergized so that technology helps the human rather than replaces it. But in the world of big business bottom lines, that is not the case. As Marco Iansiti and Karim Lakhani have observe, “Machine learning will transform the nature of almost every job, regardless of occupation, income level, or specialization. Undoubtedly, AI-based operating models can exact a real human toll. Several studies suggest that perhaps half of current work activities may be replaced by AI-enabled systems. We shouldn’t be too surprised by that. After all, operating models have long been designed to make many tasks predictable and repeatable. Processes for scanning products at checkout, making lattes, and removing hernias, for instance, benefit from standardization and don’t require too much human creativity” (18).

Education, however, can be tricky. The human centered nature of the enterprise means that the integration with the data and algorithm driven processes of machine learning are not intuitive. Heart and imagination must guide these processes. Given that the AI sea-change is affecting the entire corporate landscape—of which Higher Ed is very much a part, for better or for worse—it’s just a matter of when Education comes to terms with making the choices that will transform the very nature of the industry. As Vikram Mahidhar and Thomas Davenport put it, companies that wait to adopt AI may never catch up (32). Indeed, the companies that adopt a “wait and see” approach as others stream on ahead may lose the critical window of action.

Education has already been dipping its toes and feeling the water temperature of the AI pool. Even if a concrete AI strategy has yet to implemented, there are a number of forays into machine learning on the part of EdTech and Higher Ed organizations. There have been attempts to develop a Netflix style Education platform where AI shapes customer goals, objects and preferences into customized course recommendations. AI can personalize and customize learning software. AI cheerleaders have also pointed to the benefits of task automation: they claim that turning over repetitive tasks such as grading, paperwork, auxiliary tasks; and managing teaching aids and resources

Yet, the central problem remains, one which AI could potentially exacerbate. Tools such as AI and ML often amplify biases, and the current bias in the Ed industry is to undervalue, undersell, and underestimate human labor. The low rates of teacher salaries is fueling the accelerated rates of the Great Resignation from teaching professions. Student achievement rates in the United States continue to fall behind those of other countries that are making a concerted investment in education. The general trend in corporate culture is to believe that labor can be replaced with technological automation. Within education, that’s just not the case. The global and national failure rates in students during the COVID lockdown proves that the human connection remains central to the enterprise, even with the challenges posed by the pandemic and technological growth. The challenge and opportunity of AI lies partly lies in our rather long standing implicit bias to devalue the human.

Sources

Erik Brynjolfsson and Andrew McAfee. “The Business of Artificial Intelligence.” How AI is Changing Work: What You Need to Know About Automation, Machine Learning and the Future of Jobs, Harvard Business Review Special Issue, November 2021, pp.20-29.

Marco Iansiti and Karim R. Lakhani. “Competing in the Age of AI: How Machine Intelligence Changes the Rules of Business.” How AI is Changing Work: What You Need to Know About Automation, Machine Learning and the Future of Jobs, Harvard Business Review Special Issue, November 2021, pp. 12-19.

Vikram Mahidhar and Thomas Davenport. “Why Companies that Wait to Adopt AI May Never Catch Up.” How AI is Changing Work: What You Need to Know About Automation, Machine Learning and the Future of Jobs, Harvard Business Review Special Issue, November 2021, pp. 32-34.