AI Helps Reduce School Dropouts

The use of artificial intelligence (AI) in education is a contentious issue. While its usage by students carries plagiarism and ethical risks, it shows significant promise elsewhere. One of the most beneficial is reducing school dropout rates.

In 2022 alone, 2.1 million students dropped out of school in the U.S. Not finishing their education could leave these individuals without valuable skills and knowledge to help them later in life, leading to broader socioeconomic problems. AI can help school systems prevent this from happening through a few different means.

1. Predict Failure Risks

Some districts have started experimenting with machine learning models to predict when students are at risk of failing. By analyzing past cases, AI can learn to detect patterns often correlated with dropping out. The technology can then alert staff when a pupil showcases these early warning signs.

Catching red flags early can lead to prompt, more effective interventions. Teachers, counselors, and parents can adjust their approach to the student’s education or ask them questions about unmet needs to improve their performance before it’s too late.

Early warning systems are not perfect. Studies show they’re most effective when a school’s dropout rate is below 10% and it has enough resources for appropriate intervention. However, any improvement is beneficial, and these models will likely grow increasingly accurate with time and development.

2. Analyze Non-Academic Risk Factors

Educational facilities can also use AI to discover and track non-academic dropout risk factors. People rarely drop out for one reason, and many reasons aren’t reflected in grades. High schoolers with depression, for example, are over twice as likely to quit school than their peers. Machine learning can uncover such trends.

Predictive models can analyze past dropout cases and monitor current students showing warning signs to find new, less obvious warnings. These may include certain social behaviors or socioeconomic backgrounds when other factors are also at play. As a result, schools can better understand what leads to dropouts.

Increasing context leads to more reliable predictions and effective interventions. Being able to spot subtle trends human experts may miss is also crucial.

3. Personalize Learning Plans

AI can reduce dropout rates beyond predicting pupils at risk of quitting. It can also minimize the chances of reaching such a point by tailoring lessons to individual learners.

Studies show that personalized education leads to higher learning enjoyment in students of all backgrounds. That engagement, in turn, reduces turnover and improves academic performance. While AI is not the only way to adapt lessons to individuals, it can make it easier by removing much of the repetitive labor from the teacher’s workload.

AI can analyze each student’s performance and behavior in different scenarios to find what works best for them. It can then alter lesson plans accordingly, leaving teachers to oversee the lesson and help pupils on a personal, human level as they work. Such a combination of tech-driven customization and human-centric support could dramatically affect school retention rates.

Possible Challenges of Reducing Dropouts With AI

As beneficial as AI can be in this context, it introduces unique concerns, too. The most prevalent are those concerning privacy.

Machine learning requires considerable amounts of data to maintain accuracy. The types of models schools would need to prevent student turnover would likewise need much information on specific children. Some may argue that this level of monitoring is a breach of privacy, especially considering minors are involved.

It’s also worth noting that 80% of K-12 schools were victims of ransomware attacks in 2023 alone. Data-hungry AI models may make educational systems an even more tempting target for cybercriminals, leading to additional privacy concerns. Attack methods like model poisoning may jeopardize the safety of the technology, too.

Finally, there’s the risk of over-relying on AI. Predictions may be inaccurate, and hallucinations can still occur in advanced algorithms. Teachers who fail to account for these shortcomings may miss crucial opportunities to connect with students or react when no dramatic intervention is necessary.

AI Poses Both Risks and Rewards in Education

In its current state, AI is risky and imperfect. However, its potential is hard to ignore. Safe, informed implementation of the technology could help schools lower their dropout rates where earlier methods failed.

As AI advances, it will become increasingly reliable and valuable in education. For now, school systems should approach it carefully but monitor its progress. The downsides are worth consideration, but the upsides may outweigh them in the big picture.