Student Services and Advising Applications

With continued science and research, educators are coming to terms with an understanding of a learner’s context utilizing trauma-informed practices, wellness and various SEL curricula and frameworks. Educators now widely recognize that this is essential to both the student’s performance and wellbeing. Despite the fact that this is often referred to as “a task for humans”, developments in AI have begun a trajectory towards providing insights, tools and processes to support what people can do to help. 

The next evolution of smart tools supporting the needs of the whole child beyond the academic accomplishments of their learner’s journey including PE/fitness, nutrition and of course social, emotional and behavioral health. Tools may also support students with learning disabilities and other challenges of access, who may need support in addition to the human resources a district can provide. New assistive technologies, based on AI, may provide neurodiverse learners with enriched and tailored opportunities to learn outside of contact hours with their teachers and other school staff.

AI has the potential to contribute to equity by making it more cost-effective to produce the accommodations, services, supports and resources that specific students need in order to thrive. And yet, technologies sometimes benefit those with advantages more than those who have experienced disadvantages, deepening inequities. Now is the time for strong policies that guide the market towards equitable AI for learning. 

Examples:

• AdmitHub: a notification system that “nudges” prospective college students to stay engaged in the enrollment process (used by Georgia State).
• Apple Health: Apple is investing more in the health/wellness market. Initially sparked by exercise trends, but with increased urgency as a response to the COVID-19 pandemic, Apple’s wearables are dedicated to monitoring performance, vitals and sleep. 
• BrightBytes: a vast data analytics solution using machine learning to populate custom dashboards for monitoring and informing decisions. 
• Fitbit (now owned by Google): with new power from Google, Fitbit is bound to continue being a frontrunner in early warning indicators from biometrics like stress, sleep deprivation, and more. 
• Mosaic by ACT: A comprehensive SEL and adaptive curriculum.
• OrCam: “OrCam MyEye is a revolutionary voice-activated device for people who are blind or visually impaired that attaches to virtually any glasses. It can instantly read to you text from a book, smartphone screen or any other surface, recognize faces, help you shop on your own, work more efficiently, and live a more independent life!”
• Otsimo: An adaptive special education content developer using an AI recommendation engine.
• Roo: a sex education chatbot from Planned Parenthood.
• Tess: a mental health chatbot that has been licensed in Monterey County. Written by psychology experts.

The transcript is often the core document used to understand a student’s learning journey, informed also by an IEP, 504 and other digests built during an intake that may have started early in a child’s academic career when registering with the school. Because AI introduces new assessment capabilities that can track students’ learning as they work and do so without a formal, separate test, students receive feedback earlier which is even more valuable for keeping special education students progressing in their learning. Early intervention from attentive machines is also becoming a trend in systems offering “early warning system” functions for students at risk of falling behind.

Guidance counselors and other administrators interpret these documentations to craft student learning pathways. More advisory models are emerging to complement or even replace traditional guidance. In some cases, homeroom or study hall has been supplanted by a classroom teacher looping with the students year to year. This high-touch approach is occurring more so in high schools, where a common teacher is overseeing the student’s growth and development in a homeroom or similar setting during the school day. Needs for social-emotional learning, postsecondary aspirations and the other details of each student are addressed and held more closely by a teacher monitoring a smaller cohort of students than the caseload of the traditional guidance counselor. 

How all these roles function under the umbrella of “student services” is already being impacted by developments in AI. Credential companies are challenging the transcript’s validity by offering new systems that support verification of accomplishments. To this end, workforce development companies are looking for ways to bridge the gap between graduation and entering the workforce, making sure talent is meeting up with expectations of new hires in various fields and that students are tracking toward those needs for postsecondary plans.

Once a student reaches high school and their postsecondary plans are heavily influencing their priorities, myriad variables have to be considered with documentation and consultation between the school staff and students on their caseload. In an ideal world, this means sharing information with other professionals outside of the school or district depending on the emerging and evolving plans of the student. 

Another priority addressed by transparency is the fear that the use of AI in guidance/advising may unfairly track students based on demographic characteristics. Policies must be defined and expanded to inform students and parents appropriately, in compliance with district, state and federal regulations. The need is to achieve a trustworthy profile for at-risk students, and then data can be tracked to determine what best serves student needs.This category will be uniquely challenging to develop and scale. While numerous companies in this sector boast AI capability, few are leveraging them in a meaningful way. One standout example of where this category is making AI gains is Kimo.ai, “an AI engine to help reskill people at scale that could work in any domain and could function at very low cost. It competes on intelligent guidance, not content, and thus they partner with many other education providers.”

Better Granularity 

Secondary and higher education produces a set of evidence of learning organized around classes and grades that leads to a traditional one-page credential (diploma) with an attached transcript. This currently serves as a proxy for purported success in the workplace. Yet, there is consistent evidence that graduates with these credentials are not prepared for the work in which they end up. A credentialed learning ecosystem, focused on documenting skills and competencies that are considered important within the workplace (and higher education, for that matter), provides the granularity for better matchmaking between employer and prospective employee, as well as better feedback loops for learners as they prepare for professional life. While classes may still exist as an organizing principle, so too may a myriad of other opportunities like work-based learning, work experiences, client-connected projects, entrepreneurial experiences, early college, and internships/apprenticeships. All of these learning experiences are best organized around a set of competencies that stack to form credentials. For example, for core competencies (often consisting of sets of standards), the typical class requirement could increase granularity by five times by dividing 24 classes into 120 credentials. 

Better Documentation

Within the existing landscape demonstrated learning is captured in a variety of decentralized and disparate ways. We see a future where every learner has a Learning and Employment Record (LER) that documents their skills, competencies, and credentials across a lifetime. Learners control these records (self-sovereignty) via a digital wallet, exhibit exemplars in a portfolio, and share relevant artifacts with prospective education or employment opportunities. Accrued during formal education and employment, these collections of demonstrated competencies provide a lifetime map of acquired capabilities. The use of credentials as part of an LER-based system increases the portability of learner records across the education and employer ecosystems.

More Relevance

Industry-recognized credentials (IRCs), where possible, should be associated with showing proficiency on core, technical, and transferable competencies. A clear connection between secondary-level learning and what is expected and needed in the workplace and/or higher education will increase the relevance of the learning experiences. Credentials that have value will support both employers and learners to more efficiently and equitably document skills. Those that earn an IRC signal competence to the employer in that sector, potentially advancing their professional careers.

Additionally, this more accurate assessment of talent benefits employers through quicker hiring, keeping new employees out of work for shorter periods of time, and reduced retraining costs. In 2020, Laffer Associates research estimated that this would provide aggregate gains to the U.S. economy of nearly $437.6 billion.

Examples:

• GreenLight Credentials: “GreenLight reduces barriers for college and employment by providing secure, simple, instant validation of official records.”
• Territorium: Higher education learner record system with assessment and guidance features.
• Velocity: “The Velocity Network is a blockchain-based open source verifiable credential exchange utility layer with its basic elements and token mechanics that provides standardized communication protocols, governance, compliance and payment rails, that enable trusted, private and secured exchange of career credentials between individuals and organizations.”
• Our Ability: “Connecting Individuals with Disabilities to Jobs using AI Technology.”
• StellarWorx: skills-based hiring platform from Opportunity@Work for employers with workers Skilled Through Alternative Routes.