Responsible AI use can help universities ensure every student gets the help they need, resulting in falling dropout rates. Schools will benefit from the higher student success rate, and the student body will benefit by achieving goals that will help them in their future careers. Here’s how to apply AI to student retention.
SwissCognitive Guest Blogger: Zachary Amos – “Leveraging AI to Predict and Reduce College Dropout Rates”
Artificial intelligence (AI) is already impacting education in many ways. Some schools are embracing it to serve students better, and many learners use it to help them with research and assignments. One of its more promising uses in this field, though, is reducing dropout rates.
Dropping out of college before finishing a degree may limit students’ opportunities in the future, but it can also be difficult for schools to predict. AI can help all parties involved through several means.
Identifying At-Risk Students
Preventing dropouts starts with recognizing which people are at risk of quitting prematurely. Machine learning is an optimal solution here because it excels at identifying patterns in vast amounts of data. Many factors can lead to dropping out, and each can be difficult to see, but AI can spot these developments before it’s too late.
Studies show early interventions based on warning signs can significantly reduce dropout rates, and AI enables such action. Educators can only intervene when they know it’s necessary to do so, and that level of insight is precisely what AI can provide.
Early examples of this technology have already achieved 96% accuracy in predicting students at risk of dropping out. Combining such predictions with a formal intervention plan could let higher ed facilities ensure more students finish their degrees.
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Uncovering Non-Academic Risk Factors
In addition to recognizing known predictors of dropout risks, AI can uncover subtler, non-academic indicators. The causes of dropping out are not always easy to see in classroom performance. For example, over 60% of college students experience at least one mental health issue, which can threaten their education. AI can reveal these relationships.
Over time, AI will be able to highlight which non-tracked factors tend to appear in students with a high risk of dropping out. Once schools understand these non-academic warning signs, they can craft policies and initiatives to address them.
Enabling Personalized Education
AI is also a useful tool for minimizing the risks that lead to quitting school before someone even showcases them. Personalizing educational resources is one of the strongest ways it can do so.
The AI Research Center at Woxsen University in India successfully used chatbots to tailor lessons to individual students. Students utilizing the bot — which offered personalized reminders about classwork — were more likely to receive a B grade or higher. People attending Georgia State University showed similar results when using a chatbot to drive engagement.
Personalized education is effective because people have varying learning styles. AI provides the scale and insight necessary to recognize these differences and adapt resources accordingly, which would be impractical with manual alternatives.
Improving Accessibility
Similarly, AI can drive pupil engagement and prevent stress-related dropout factors by making education more accessible. Many classroom resources and university buildings were not designed with accessibility for all needs in mind. Consequently, they may hinder some students’ success, but AI can address these concerns.
Some AI apps can scan physical texts into digital notes to streamline note-taking for those with impairments limiting their ability to use pens or keyboards. Natural language processing can lead to better text-to-speech algorithms for users with vision impairments. On a larger scale, AI could analyze a campus to highlight areas where some buildings or walkways may need wheelchair ramps or other accessibility improvements.
Responsible AI Usage Can Minimize Dropout Rates
Some applications of AI in education — largely dealing with students’ usage of the technology — have raised concerns. The technology does pose some privacy risks and other ethical considerations, but as these use cases show, its potential for good is also too vast to ignore.
Responsible AI development and use can help universities ensure every student gets the help they need. As a result, dropout rates will fall. Schools will benefit from the higher student success rate, and the student body will benefit by achieving goals that will help them in their future careers.
About the Author:
Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.
Responsible AI use can help universities ensure every student gets the help they need, resulting in falling dropout rates. Schools will benefit from the higher student success rate, and the student body will benefit by achieving goals that will help them in their future careers. Here’s how to apply AI to student retention.
SwissCognitive Guest Blogger: Zachary Amos – “Leveraging AI to Predict and Reduce College Dropout Rates”
Dropping out of college before finishing a degree may limit students’ opportunities in the future, but it can also be difficult for schools to predict. AI can help all parties involved through several means.
Identifying At-Risk Students
Preventing dropouts starts with recognizing which people are at risk of quitting prematurely. Machine learning is an optimal solution here because it excels at identifying patterns in vast amounts of data. Many factors can lead to dropping out, and each can be difficult to see, but AI can spot these developments before it’s too late.
Studies show early interventions based on warning signs can significantly reduce dropout rates, and AI enables such action. Educators can only intervene when they know it’s necessary to do so, and that level of insight is precisely what AI can provide.
Early examples of this technology have already achieved 96% accuracy in predicting students at risk of dropping out. Combining such predictions with a formal intervention plan could let higher ed facilities ensure more students finish their degrees.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Uncovering Non-Academic Risk Factors
In addition to recognizing known predictors of dropout risks, AI can uncover subtler, non-academic indicators. The causes of dropping out are not always easy to see in classroom performance. For example, over 60% of college students experience at least one mental health issue, which can threaten their education. AI can reveal these relationships.
Over time, AI will be able to highlight which non-tracked factors tend to appear in students with a high risk of dropping out. Once schools understand these non-academic warning signs, they can craft policies and initiatives to address them.
Enabling Personalized Education
AI is also a useful tool for minimizing the risks that lead to quitting school before someone even showcases them. Personalizing educational resources is one of the strongest ways it can do so.
The AI Research Center at Woxsen University in India successfully used chatbots to tailor lessons to individual students. Students utilizing the bot — which offered personalized reminders about classwork — were more likely to receive a B grade or higher. People attending Georgia State University showed similar results when using a chatbot to drive engagement.
Personalized education is effective because people have varying learning styles. AI provides the scale and insight necessary to recognize these differences and adapt resources accordingly, which would be impractical with manual alternatives.
Improving Accessibility
Similarly, AI can drive pupil engagement and prevent stress-related dropout factors by making education more accessible. Many classroom resources and university buildings were not designed with accessibility for all needs in mind. Consequently, they may hinder some students’ success, but AI can address these concerns.
Some AI apps can scan physical texts into digital notes to streamline note-taking for those with impairments limiting their ability to use pens or keyboards. Natural language processing can lead to better text-to-speech algorithms for users with vision impairments. On a larger scale, AI could analyze a campus to highlight areas where some buildings or walkways may need wheelchair ramps or other accessibility improvements.
Responsible AI Usage Can Minimize Dropout Rates
Some applications of AI in education — largely dealing with students’ usage of the technology — have raised concerns. The technology does pose some privacy risks and other ethical considerations, but as these use cases show, its potential for good is also too vast to ignore.
Responsible AI development and use can help universities ensure every student gets the help they need. As a result, dropout rates will fall. Schools will benefit from the higher student success rate, and the student body will benefit by achieving goals that will help them in their future careers.
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