Colleges and universities are gaining students, tuition costs are rising, and students’ educational and economic needs are evolving because of the changes brought on by the digital age. Nonetheless, the textbook continues to adopt the one-size-fits-all approach to teaching despite the diverse learning styles and aptitudes of students. However, an alternative to the standard physical textbook exists: the eBook. The digital text is not only more generally cost-effective for students, but eBooks can be manipulated by the student for greater accessibility. Students can access all their courses’ texts with one device, such as a laptop or tablet, altering the presentation of the text and highlighting key points without the back-breaking struggle of carrying physical textbooks and juggling multiple pens.
Yet one disadvantage to the eBook is the digital platform itself. Unlike the physical textbook, the eBook reduces the level of tangibility and visual engagement. So, the question arises: how does one keep students engaged with a digital text without tempting them to deviate to other digital outlets on the same device? The collaboration between artificial intelligence (AI) and adaptive learning (AL) is the answer to student engagement, accessibility, and improved educational experience.
Image Source: Wallpaper Flare
Tracie Bryant, SLED Manager at Zoom Video Communications, defines AL as a technology that can present information in the best way possible for each student based on information acquired through learning analytics. Learning analytics is data extracted from reporting tools that are used to develop a learner profile for each student. The data allows educators to improve the structure of their courses and quickly adapt the material, whether manually or via AI, to better the learning experience of their students. Open University, for example, developed a learning analytics system called OU Analyse that allows educators to predict student success. The system collects students’ user data to determine the student success rate based on factors like assignment submission, test scores, and online activity.
Example of a learning analytics dashboard (Image Source: OU Analyse)
The current transition from in-person to online learning is motivation for the improvement of AI’s use in digital education. AI shows promise in benefiting the educational experience of both instructors and students through AL. A common example of AI’s role in current education is how AI automates software that can mark tests, distribute course material, and evaluate submitted assignments for plagiarism and formatting requirements. Ryerson University’s Crowdmark is one learning analytics system that allows for online collaborative marking between instructors and teaching assistants, provides integrated grading tools, and can distribute and collect student work.
The AI of learning analytics also makes data mining more accessible for institutions. Data mining key areas of a course’s learning analytics obtained from students expands the possibilities for digital learning platforms to conform to the diverse learning styles of students. Pearson’s MyLabs collects student assessments and reacts to how students are performing by offering data-driven guidance to help instructors improve students’ understanding. Likewise, McGraw-Hill’s LearnSmart is also a learning analytics system that works by assessing students’ knowledge and skill levels. The system actively tracks students’ performance to then determine areas where students experience memory degradation and reinforce concepts to improve comprehension. These are just some established examples of how publishing companies are responding to the need for AL systems and online learning.
Image Source: Wiley Education Services
AI seems to have many advantages for students and instructors, but the difficulty lies in motivating publishing companies to adapt and shift to predominantly digital publishing for their revenue stream. eBooks and AI experiences may be more cost-effective for students, but the transition to digital poses a monetary problem for publishers. Cognizant writers Chris Lawrence, David Ingham, and Joydeep Sinha note how developing eBooks and learning management systems (LMS) require a steep investment in content development in order to re-engineer content, transform content workflows, and alter the commercial model for maximum profitability.
The transition to a digital platform may seem daunting for publishers, but the writers of Cognizant also point out how the investment is worthwhile. Digital publishing and LMS development allow publishers to create a new revenue stream while refreshing existing brands and products for online use. Increased use of an LMS that integrates AL and other AI technology with learning analytics improves the consumer experience and allows publishers to justify pricing while revealing areas for potential future revenue.
As online learning becomes more mainstream in colleges and universities, publishers are becoming more ambitious in adapting to the needs of their consumers. A digital platform that uses AI to customize education for students and instructors is already at work in many institutions. Publishers are investing in and developing digital publications and products, slowly but surely building a future where education is personalized, accessible, and practical. AI developed for education will evolve the academic world and change education into an experience that is customized and refined for each student.
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