Every higher education institution strives to offer a unique experience for its students. While experiences will vary from one institution to another, they all aim to transform students through building knowledge.
That mission has faced an increasing number of challenges over the past decade. Some of the challenges are driven by changing views of what a post-secondary degree means and shifts in what careers look like for Gen Z and younger students. These challenges have led to dwindling enrollments and disengaged students.
Big data and AI solutions like chatbots and personalised learning software are potential solutions to these challenges. But how effective are they, and what are the implications of implementing them? Here are three things universities and colleges should consider when deciding how to use big data and AI.
AI and Big Data Can Help With Personalised Learning
More and more institutions are replacing the traditional one-size-fits-all approach to teaching with more personalised options for students. The demand for personalised learning is driven by the recognition that students have different learning styles, accessibility needs and demographic backgrounds—all of which influence how they learn.
Personalised learning provides a tailored experience that can help engage students struggling to connect with existing materials. This is an advancement from the old days of asking whether someone was a visual or auditory learner. Still, a significant amount of personal student data is required to power AI and machine learning algorithms.
The power of personalised learning comes from identifying how students are progressing through a lesson and making any necessary adjustments, or suggesting different materials to help them succeed. These tailored experiences use data from past academic performance and real-time input from the classroom to provide helpful insights.
The potential benefits go beyond the students. In large classes or online courses, professors and teaching assistants can often miss cues on potential issues with students. An Educause survey reported that 22% of schools said they planned to use machine learning and big data tools to help identify academically at-risk students. Personalised learning tools provide alerts on students who are struggling so that problems can be tackled before they become larger academic issues.
AI Can Connect Students and Institutions
In addition to personalised learning, finding new ways to connect with students is a critical issue for higher education institutions. Today’s students come to colleges and universities with different expectations than did previous generations with respect to how they want to interact with their institutions and instructors. These students have grown up in an era where 24/7 access to information isn’t just nice to have—it’s a requirement.
It’s hard to believe, but most students entering university this fall were born in 2004. They’ve come of age in an era where every conceivable piece of information is available with a few taps on a rectangular screen that everyone has in their pocket. They’ve never looked up movie times in a newspaper or called an operator to ask for a phone number. They’re also accustomed to getting answers on demand through the use of chatbots.
AI and machine learning advances have dramatically increased the effectiveness and ease of using chatbots. These improvements are driving the growth of chatbots in everything from e-commerce to government services. A 2020 report from sales tech leader Drift indicated that chatbot usage had increased 92% since 2019.
Chatbots are being deployed in higher education institutions to help students get answers to questions on housing, tuition, schedules and more. Being able to ask a chatbot can be easier for some students, too. In a paper from the Proceedings of the 4th International Conference on Internet Science, authors Petter Bae Brandtzaeg and Asbjørn Følstad noted that 5% of those surveyed preferred chatbots to humans because they didn’t worry about “feeling stupid when asking important questions.”
Universities Can’t Lose Sight of Ethical, Legal and Privacy Implications
Before implementing personalised learning, chatbots or other AI-powered solutions, higher education institutions should consider the risks and responsibilities of collecting and using personal data from applicants, students and staff.
AI solutions only work when there’s a broad set of real-world data powering them. But that data comes with ethical, legal and privacy concerns regarding how it is collected and used. It’s critical for higher education institutions to have a transparent and detailed data collection and use policy.
Depending on where the institution or students are located, AI and big data-powered educational tools will also need to comply with privacy legislation such as GDPR in Europe and CCPA in California. These laws require software makers to enable students to request a download of the data collected on them and the ability to have that data removed from a system.
Another important consideration is that AI tools draw from existing patterns and data sets—many that reflect the inequalities of today’s current environments. Data sets are often based on limited samples that don’t include a diverse cross-section of the students being served. When speaking with solution providers, higher education leaders should keep this front of mind.
If universities can find a way to collect and use student learning data fairly, students stand to gain tremendously from a more personalised approach to education that targets their strengths and, in some cases, preempts their failures.