1- Responsible AI coaching should adapt to individual student circumstances.
Design clear mechanisms to balance student access to human coaches.
Why?
Some coaching tasks are more suitable for human contact. Refer to coaching best practices to determine whether a student would benefit from higher contact with human coaches. Clear mechanisms should be implemented to ensure that students can easily access human coaches when necessary.
Examples:
Implement a prominent 'Request Human Coach' button in the AI interface.
Develop a feedback form for students to express their need for human interaction.
Set up automatic escalation for discussions involving sensitive topics such as self-harm or academic probation.
Develop a user preference setting for the frequency of human coach check-ins.
Consider creating mechanisms for students to set their own goals, preferences, and boundaries within the AI coach.
Why?
Consider the importance of preserving and enhancing student agency, and explore how the tool may offer features that support this.
Examples:
Frequency of check-ins or reminders
Topics they're comfortable discussing with AI vs. human coaches
Deadline sensitivity (how early to start reminding about due dates)
Level of detail in explanations (brief overviews vs. in-depth discussions)
Cultural or religious considerations in advice and examples
Privacy settings for sharing their data
2- Students, for various reasons, may refrain from discussing sensitive topics with the AI coach.
Provide assurances about privacy and security of any communications with the AI coach.
Why?
Students' willingness to discuss sensitive topics with AI coach may be influenced by their perception of AI.
Examples:
Allow students to define their comfort levels for discussing different sensitive topics.
Provide privacy statements, be clear about when and how student conversations are saved within the system.
Allow students to delete parts of their conversation.
Identify a list of sensitive topics that would be automatically escalated to human coaches.
Why?
AI responses to sensitive topics, if not carefully crafted, could potentially exacerbate student discomfort or stigma.
Examples:
Mental health concerns (depression, anxiety, suicidal thoughts)
Physical health issues affecting academic performance
Substance abuse or addiction
Sexual harassment or assault
Discrimination or bullying
Family crises or domestic violence
Financial hardships affecting studies
Eating disorders
Struggles stemming from gender identity or sexual orientation
Grief and loss
Academic integrity violations
Visa or immigration status issues
Pregnancy or childcare challenges
Traumatic experiences affecting academic life
Severe academic struggles or potential dismissal
Design a mechanism to flag underlying crisis or reasons for human review.
Why?
Students often do not explicitly explain the underlying reasons for their questions and those might be left ambiguous. The system should afford students to flag and contest responses and request direct action for a variety of reasons. However, sometimes students might feel reluctant to take direct action, design mechanisms to automatically flag such cases and reach out to students.
Examples:
Keyword analysis for crisis-related terms or phrases
Detecting sudden changes in student activity patterns
Sentiment analysis to detect prolonged negative emotions
Tracking sudden drops in academic performance or engagement
Monitoring for repeated questions about coping strategies
Detection of social isolation indicators in student's descriptions
Detecting expressions of hopelessness or worthlessness
3- AI coaching should accommodate diverse levels of familiarity with college-related terminology.
In the absence of a human coach, readily make definitions and terms available to students as they appear in conversations with the AI coach.
Why?
Students may encounter unfamiliar terminology during AI coaching sessions, potentially hindering their understanding if immediate clarification is not available.
Maintain a balance between providing necessary explanations and encouraging students to express need for clarifications.
Why?
Students may be hesitant to express unfamiliarity with terms when they don’t understand something. Finding the right balance between offering explanations and prompting students to ask questions helps prevent misunderstandings and promotes active engagement.
4- Students might have different preferences for conversation tones and communication styles.
Evaluate whether different tones or adapting tones are useful strategies for your student body across different demographics.
Why?
The formality level of communication can impact a student's comfort and receptiveness.
Examples:
Enable students to set their preferred communication tone (formal, casual, motivational, etc.).
If assumptions are made based on existing data, the system should transparently communicate those assumptions.
Consider creating a range of AI communication styles for different coaching contexts, while allowing students to customize their preferences.
Why?
The most effective tone for AI coaching may vary based on the subject or task. Also, some tones such as humor might be inappropriate for some people or tasks.
Examples:
A list allowing students to choose their communication style
Referring to coaching best practices to automatically adopt a tone for a specific task.
A bot that might simulate empathetic responses towards students.
5- Focus on developing students' problem-solving skills and decision-making abilities, rather than simply providing ready-made answers.
For appropriate tasks, refer to coaching best practices to create interactions that motivate students to engage in problem solving.
Why?
Collaborative problem-solving and student agency are essential in student success.
Provide resources and information that enable students to take action independently when appropriate.
Why?
Coaching is about empowering students to make their own decisions.