Ethical Considerations of AI in Classroom Settings and Student Privacy

What if the same AI technological systems that assist learners with their educational tasks at hand also carefully monitored their every action in real time? Welcome to the dual nature of AI in education.  

AI Technology is transforming the classroom and education as a whole, with the development of tailored learning experiences, performance monitoring, automated grading, and even behavior tracking. But, as such technology becomes intimately integrated with the learner’s teaching and learning processes, questions emerges such as, are we indeed destroying student privacy in the name of progress? And, are we applying sufficient critical thinking to the ethics and morals of the aiding systems?  

This blog focuses on the ethics of AI's role in the classroom, specifically concerning the privacy of student information, algorithmic discrimination, and consent. Adopting new technologies has always been exciting for schools, but in this era where the rush to adopt new tools and devices overshadows their value, discernment integrity and compassion has never been more sought after.  

AI in the Classroom: The Upside  

There are numerous risks associated with the topic at hand, but let us not forget the value that AI contribute to education:  

• AI adapts to learners in real time through adaptive learning algorithms.  

• Students receive immediate AI tutor assistance after class hours.  

• Predictive analytics enable educators to proactively assist struggling students in advance.  

• The automation of grading allows for greater engagement with students personally.

Knewton Carnegie Learning and Smart Sparrow employ AI to optimize processes, assisting learners in advancing through intricate subjects seamlessly on their own timelines.  

While these strategies boost learner achievement and productivity, they come with ethics and data privacy challenges as their trade-offs.  

The Ethical Dilemma: AI Interaction with Student Information Privacy  

AI technology comes armed with a voracious appetite for data. It's only as competent as the staggering amount of information about students fed to it. For example:  

• Tracking academic achievements and data related to learning milestones.  

• Biometric information, including facial expression analysis, eye movement monitoring, and even tracking of typing patterns.  

• Personal and social behavior including attendance, discipline history, and cumulative conduct.  

• Data on the geography of school/workplace and the device employed such as a smart phone, laptop, or tablet.  

This information, if left unchecked, can be manipulated, misused or monetarily exploited with little oversight. This is precisely where the central ethical issues start to form.  

1. Consent for the use and disclosure of private information as well as disclosure of relevant information Approximately one of the greatest fears grounded in ethics is whether students, together with their guardians, receive proper notification about the types of information collected and in what manner it will be used.  

⚠️ Problem:  

Numerous AI powered applications being used lack clear, overt rules outlining their operations. They mostly work in the foreground without clear guidance provided to the user. These users—often children—do not understand the nature of the tools they are interacting with.

✅ Ethical Best Practice:  

Schools need to put in place a process for obtaining consent that is appropriate for the child’s age, instructing parents and students on the ways AI systems work, what kind of information is collected, and how it is secured.

2. Data Privacy and Security  

AI applications maintain personal and sensitive information about students, making this data a target for cyber attacks, information leaks, or unauthorized third party access for selling the data.  

⚠️ The Problem:  

More than 1,000 school districts in the United States suffered data breaches during the year of 2021. The careless actions of one AI vendor can lead to the exposure of the private details of thousands of students due to poor data management.  

✅ Example:  

Not every educational technology company is bound by the Family Educational Rights and Privacy Act (FERPA) which offers privacy safeguards to the student’s education records. Vendors who school districts contract with need to be carefully screened for minimum requirements regarding data compliance and proper data encryption safeguards to ensure data is maintained.

3. Algorithmic Bias and Fairness  

The efficiency of an AI model is directly correlated with the quality of data used to train it. If the data used for training contains prejudice, the AI engine is more likely to be biased.  

⚠️ The Problem:  

Consider a hypothetical scenario where a predictive analytics solution recommends a student for a high risk of dropping out based solely on their geographic location, their ethnicity, or disciplinary history with no regard for their surroundings or progressive change.

Stereotypical Digital Profiling  

In some school districts, the use of AI discipline systems has resulted in a disproportionate number of minority students being flagged. This further exacerbates unfounded and harmful stereotypes.  

Under Ethical Solutions  

Required implementing bias audits, and ensure that AI recommendations are never the sole basis for high-stakes decisions.  

A Surveillance and Autonomy Issue.  

Some AI programs focus on learning and adapting to students but transcend to actively tracking student behavior—measuring “engagement” by monitoring facial expressions, tracking eye movements during tests, or scrutinizing social media interactions.  

The Concern  

Students might feel that they are being surveilled by robots which subsequently alters their perception of life and worsens mental health.  

Example  

During the pandemic, ProctorU and ExamSoft were accused of using AI-driven proctoring tools that flag students in a discriminatory fashion and record them during remote testing.  

An Ethical Consideration  

Students must be provided with options to invasive monitoring, and advanced systems of behavioral control should be subjected to assessment by humans prior to meting out punitive actions.  

The Technology Gap, Equity and Inclusion  

There is an issue of inequity and inclusion when students do not have the same levels of access to AI educational resources. Students who come from low-income, rural and underfunded schools are left behind as AI sets the norm.  

The problem Example  

The gap in educational inequality could expand if AI is perceived as a luxury gadget limited to those rich enough to afford high-end devices and blazingly fast internet connectivity.

✅ Optimal Ethical Boundaries:

It is imperative that education technology (edtech) developers and policy makers prioritize the creation of multilingual tools that can perform offline and function on inexpensive devices.

Guidelines for Responsible Artificial Intelligence Practices in Education

Principle Responsible Action

Transparency Comprehensive justification on the purposes of data collection and the AI’s interpretation.

Consent Explicit, voluntary, informed, and retractable agreement by students and guardians.

Privacy FERPA, GDPR, and relevant national laws regarding data privacy and protection are respected and followed.

Equity There are no biases present in algorithms trained on representative sample data.

Foster Prospective Well-Being of Students Surveillance should not be constant; emotional and social learning should be nurtured.

Barriers to Access Do not diminish opportunities for AI usage, and work to lessen the divide in technology access.

Future Vision: Deeply integrated Human and AI Collaboration in Learning

The issue does not revolve around whether to incorporate AI in the classroom; rather, it has already been adopted.

Whom should the technology serve? Answer: The purpose driven human-centered design guarantees that stakeholders, active teachers and students, citizens, guardians, and AI system builders are protected at all levels and advocate for alleviating scrutiny of all systems.

It should be prioritised that the focus is placed on AI solutions having minimal negative impact, but rather defining the foundational guidelines of autonomous AI-based systems in equitable education opportunities sustainably.

Takeaway: Let Us Teach Responsibly  

The potential of A.I. tools in education is immense, but without careful consideration, it can lead to eroding privacy, accentuating existing biases, or creating further digital divides. Careful consideration must be taken in A.I. development and deployment so that learning frameworks benefit students. Ethically integrated A.I. can make learning frameworks inclusive and effectively centered on the students.  

As we foster the ‘classroom of the future,’ it is critical to remember that while data and analytics provide insight, the dignity of a person is invaluable.