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SupportFrom Classroom to Career: Why QuantHub’s CRI Pathway Gives Alabama CTE Students a New Option
For Alabama CTE students, earning a Career Readiness Indicator (CRI) is an important step toward graduation and demonstrates skills that can help prepare them for college and the workforce. Choosing the right CRI can also give students meaningful exposure to career pathways and skills they can use beyond the classroom.
QuantHub’s CRI pathway combines credentialing with practical data and AI skills connected to today’s in-demand careers. Students build workforce-ready skills, while educators gain an easy-to-implement pathway and opportunities to strengthen their own AI knowledge and earn professional development credit.
In this article, we’ll explore the advantages of QuantHub’s CRI pathway through the real-world experience of Paula Hughes, an Alabama public school teacher who used QuantHub to give her students exposure to modern careers and build practical AI and data skills. Paula also shares the benefits for educators, from easy classroom implementation to professional development opportunities.
A CRI Opportunity That Can Start With Intro-Level Students
For CTE educators, one major advantage of QuantHub’s CRI pathway is that students can begin working toward a credential earlier. Hughes said her traditional upper-level engineering CRI can require roughly two years of preparation, while QuantHub gives her intro-level students the opportunity to work toward a CRI within a single school year.
The pathway is available at no cost to ALSDE schools, making it easier for teachers to implement without adding another program expense. Students who complete every required module at 100% are eligible to sit for the end-of-pathway CRI credentialing exam, which is also offered at no cost and provides multiple attempts to earn the credential.
For Hughes, removing the exam cost can open the opportunity to more students. With other certifications, she explained, other CRI pathway exams can cost around $90 per student, making teachers hesitant to let a student test unless they are confident they will pass. However, the QuantHub CRI pathway exam is offered for free to all students.
The process still requires commitment. Students must complete the full pathway before taking the exam, helping them practice managing a longer-term goal rather than simply completing individual assignments.
“It’s not just the grade that matters,” Hughes said. “It’s the actual completion, you know, of this big, long process.”
For Students: A Credential Connected to the Skills Employers Want
For Hughes, earning the credential is only part of the value. The content itself exposes students to skills they may not encounter elsewhere in their existing curriculum—particularly AI, data, and emerging technology.
“Whether that’s college or whether that’s career, you know, especially AI, they are going to have to know how to work with that and with the technology in order to really get good and valuable jobs,” Hughes said.
She has made those connections tangible by showing students actual career opportunities in the Birmingham area that call for skills related to what they are learning through QuantHub.
Hughes said she showed her students examples ranging from jobs accessible with a high school diploma and relevant skills paying as much as $50 per hour, to opportunities for recent college graduates paying around $80,000 per year. She also showed them examples of positions paying more than $100,000 annually with additional experience. The exercise changed how students viewed the material.
“Once we started looking at those jobs and what they pay, I had a lot of students saying, ‘Oh my gosh, I didn’t know this was an opportunity,’ and especially in Birmingham,” Hughes said.
That real-world connection also extends beyond the CRI itself. Hughes discusses resumes with her students and shows them how QuantHub badges can be added to LinkedIn profiles. She has also seen growing student interest in internship opportunities connected to these skills.
For students who are not pursuing an AI or data-specific career, the learning can still be valuable. Hughes has older students complete AI literacy content alongside their project-based coursework, and she said students were particularly interested to learn that similar AI literacy material is being used at the college level.
For CTE Teachers: A CRI That Fits Into the Classroom
Adding another credential can sound daunting when teachers already have a full curriculum to cover. Hughes has found that QuantHub does not have to take over her class schedule.
“It’s also just so easy to integrate into the classroom because, you know, I have a full curriculum, and I’m sure most teachers do as well,” Hughes said.
Her solution is simple: five to 10 minutes at a time. Hughes uses QuantHub as a bell ringer. Students enter class, open their computers, and begin the assigned QuantHub work. She typically gives them five minutes—and sometimes 10—depending on the week. By following the pacing she has established, students can steadily work through the pathway without sacrificing an entire class period each day.
She can also designate occasional full “QuantHub days” when students need additional time to progress.
For her beginning students, Hughes uses QuantHub’s Schoology integration to assign the pathway step by step. “This way, they can just click on the assignment in Schoology, and it takes them exactly to the module I want them to do,” she explained.
For educators, the integration can also simplify classroom management. Hughes said teachers can roster students through Schoology, assign lessons, collect grades when desired, and monitor student progress without requiring students to manage another set of login credentials.
“It is very easy for a teacher to go through Schoology and assign these lessons,” Hughes said. “It’s integrated, it’s easy, it’ll grade it. You know, it’s just a very easy way to keep track of what your students are doing.”
Removing a Major Barrier: The Cost of Credentialing
Cost can be another consideration when choosing a CRI.
Hughes estimates that the exam associated with her upper-level Inventor certification costs around $90 per student, although she noted that the exact amount should be verified. While the state can reimburse those costs, the exam still has to be paid for.
That expense inevitably affects how educators think about who is ready to test. “You would never allow a student to take that exam unless you were a hundred percent sure they could pass it, you know, because it’s expensive,” Hughes explained.
The QuantHub CRI exam, by contrast, is offered at no cost to participating ALSDE schools. For Hughes, that creates an important opportunity for students who might otherwise be overlooked. “Since it’s free, it gives some students an opportunity that maybe wouldn’t get that opportunity because, you know, maybe they’re not a top-performing student in the classroom, but they can take this CRI because they get that opportunity to take it.”
The financial advantage extends beyond the exam itself. Hughes said her county coordinator appreciates avoiding the reimbursement process associated with paid credentialing options.
Professional Development That Benefits Teachers, Too
The learning does not stop with students. Hughes has completed QuantHub learning herself, and she said doing so has strengthened both her ability to help students and her own understanding of AI.
“I was shocked at how little I knew when I got in this platform and started doing my own PD,” she said. “I feel like I’ve learned a lot because, again, this is information I did not know myself.”
She now uses AI outside of the classroom for everything from researching major purchases and reviewing contracts to troubleshooting things that break. “I use it a lot and I did not even know what all it could do before I got myself educated through QuantHub on some of these AI things,” Hughes said. “So I just think it’s valuable for everyone.”
For CTE educators specifically, there is another potential benefit: professional development. Hughes explained that career tech teachers are required to complete annual technical update hours, and she has been able to use her QuantHub professional development toward those requirements.
That means the same platform can help educators build their own AI and data fluency, become more confident supporting students, and work toward professional learning requirements—all while preparing to implement the CRI pathway in their classroom.
Why CTE Teachers Should Consider QuantHub’s CRI Pathway
For CTE educators evaluating CRI options, the question is not simply whether a credential meets a requirement. It is also what students have to gain from the process of earning it.
QuantHub gives educators a way to introduce an industry-recognized credential opportunity to students earlier in their CTE journey while building AI and data skills tied to a rapidly changing workforce. The pathway can be incorporated into an existing classroom routine in small increments, supported through LMS integration, and offered without adding a credentialing exam cost for students or schools.
Hughes sees that combination as something her students simply did not have before. “This is why we are doing it, because it’s information that’s so valuable and so applicable to, you know, the real world and especially their next phase of life,” she said.
For Alabama CTE teachers deciding which CRI opportunity to bring into their classrooms, that creates a compelling proposition: a credential students need, built around skills they will actually use, delivered in a way teachers can realistically implement.
Not sure where to get started?
QuantHub is here to help! Schedule a support call with a QuantHub team member to walk you through the free onboarding process and get started using QuantHub’s CRI pathway in your classroom HERE at QuantHub Support Center.
OR reach out to a team member directly at: support@quanthub.com
Inside QuantHub’s Free CRI Pathway for Alabama CTE Students
What Is AI Literacy Training in Higher Education?
AI literacy training in higher education teaches students, faculty, and administrators how to understand, evaluate, and responsibly use artificial intelligence in academic and professional settings. It develops practical skills for working with AI while building the judgment needed to assess AI-generated information, recognize limitations and risks, and determine when human expertise is necessary.
For colleges and universities, AI literacy goes beyond learning individual tools. It develops transferable knowledge and skills that learners can apply as AI technologies and their uses continue to evolve.
Why Does AI Literacy Matter in Higher Education Right Now?
Higher education has long emphasized digital skills, but AI requires more specialized knowledge and judgment. Students need to understand how to interact with AI, critically evaluate its outputs, recognize unreliable information, and apply human expertise when using AI-generated results. Developing these capabilities moves learners beyond basic tool use toward AI-specific fluency.
Three pressures make these skills increasingly important: employer expectations, accreditation trends, and student demand for education that reflects the technologies shaping their fields. Employers increasingly expect graduates to combine subject-matter knowledge with the ability to work effectively with emerging technologies, while students need opportunities to understand how AI is changing the disciplines and professions they are preparing to enter.
Accreditation standards reflect similar priorities. AACSB’s 2026 Global Standards for Business Education emphasize continuous curriculum renewal and call for curricula to develop learners who can adapt to evolving digital, analytical, and information technologies. This connects AI literacy to digital agility—the broader institutional capability to adapt as technologies and practices evolve. AI literacy provides the AI-specific knowledge and judgment that support that capability. AACSB Global Standards for Business Education
What Does AI Literacy Training Actually Include?
Effective AI literacy training combines foundational knowledge with practical application, critical thinking, and responsible use. It teaches learners not simply how to operate AI tools, but how to understand and evaluate them as technologies change.
Core components include:
- Core AI concepts and terminology: Learners develop a working understanding of artificial intelligence, machine learning, generative AI, large language models, algorithms, training data, and automation. The goal is enough foundational knowledge to understand how AI systems operate, what they can do, and where limitations exist.
- Practical tool fluency: Learners gain hands-on experience interacting with AI systems, refining prompts and approaches, selecting appropriate tools, and applying them to relevant tasks. This experience develops AI fluency—the ability to put AI knowledge into practice effectively.
- Critical evaluation: AI-generated information can be inaccurate, incomplete, biased, or unsupported. Learners practice questioning outputs, verifying information, evaluating evidence, and determining when human expertise should take priority.
- Responsible and ethical AI use: Training addresses privacy, bias, transparency, intellectual property, security, academic integrity, and accountability. UNESCO’s AI Competency Framework for Students identifies four core dimensions—human-centered thinking, AI ethics, AI techniques and applications, and AI system design—while emphasizing critical judgment and responsible engagement with AI. UNESCO AI Competency Framework for Students
- Data literacy fundamentals: Because AI systems depend on data, learners need to understand data quality, interpretation, evidence, and bias. Institutions can Build data and AI fluency at every level by developing these connected competencies.
- Discipline-specific application: Learners apply AI knowledge within business, humanities, STEM, healthcare, education, and other fields, connecting foundational skills to the tools, decisions, and ethical questions relevant to each discipline.
Who Needs AI Literacy Training in Higher Education?
Students
Students need AI literacy to use AI effectively while maintaining their own critical thinking and judgment. They should know how to evaluate AI-generated information, recognize errors and bias, protect sensitive information, follow academic policies, and determine when AI is appropriate for a task.
Students also need opportunities to apply these skills within their disciplines. An accounting student evaluating AI-generated financial information encounters different applications and risks than an engineering student using AI-assisted code or a humanities student evaluating generated text.
Faculty
Faculty need AI literacy to make informed decisions about teaching, assignments, assessment, research, academic integrity, and discipline-specific applications. They also need to help students distinguish productive AI use from uses that interfere with learning.
Faculty development should address practical AI skills and pedagogy, giving instructors the knowledge to evaluate emerging tools, establish expectations, and incorporate relevant applications while preserving learning objectives.
Administrators
Administrators need AI literacy to guide institutional decisions involving governance, policies, technology investments, privacy, security, academic standards, and faculty support. This knowledge helps leaders establish consistent approaches to AI across programs rather than leaving policies and practices entirely to individual departments or instructors.
AACSB’s 2026 framework for AI in business education documents how business schools are moving beyond isolated AI experiments toward integration across teaching, learning, research, and institutional strategy, reinforcing the need for AI understanding beyond the classroom alone. AACSB: A Framework for Artificial Intelligence in Business Education
What Is the Difference Between AI Literacy and Related Terms?
Digital literacy is the broad ability to use, understand, and evaluate digital technologies and information. It includes skills such as navigating digital environments, evaluating online sources, communicating through digital platforms, and using software effectively.
AI literacy is more specific. It includes understanding foundational AI concepts, using AI tools appropriately, critically evaluating generated outputs, recognizing risks and limitations, and making responsible decisions about when and how AI should be used.
AI literacy is also related to AI fluency. Literacy establishes the knowledge and critical understanding needed to work with AI. Fluency reflects the ability to apply that understanding effectively across tasks, adapt AI use to different contexts, and evaluate the resulting outputs.
An AI certification is different. It is a credential demonstrating completion of a defined course, assessment, or set of competencies. Certification can provide evidence of learning, but the credential itself does not define literacy.
These concepts operate at different levels. AI literacy focuses on the knowledge and judgment individuals need to engage with AI, while broader institutional adaptability concerns how colleges and universities respond as technologies and practices evolve. Effective AI education therefore develops transferable knowledge and judgment rather than proficiency with one platform or tool.
How Do Colleges and Universities Implement AI Literacy Training?
Colleges and universities can implement AI literacy through standalone courses, embedded curriculum, platform-based training, or a combination of approaches. Standalone courses establish shared foundations, while embedded approaches place AI learning inside existing disciplines. Platform-based training can provide consistent learning across multiple courses or departments.
One example is QuantHub, which institutions can integrate into existing programs to provide structured, discipline-specific data and AI learning.
Examples include:
- Department-wide integration: The University of Alabama’s MIS program incorporates the platform across six core courses, embedding AI alongside programming, databases, analytics, and systems analysis.
- AI-assisted coursework: Alabama MIS students learn to evaluate AI-generated code, work with retrieval-augmented generation (RAG) and prompt engineering, and apply human judgment to AI outputs.
- Embedded marketing curriculum: Marketing in the Age of AI content can be incorporated into existing marketing courses, connecting AI to research, strategy, content, personalization, and campaign optimization.
- AI-focused assignments: Marketing assignments can require students to document prompts, evaluate generated results, and explain their own interpretations.
The University of Alabama MIS case provides a concrete example of this model: AI is integrated across MIS 200, 221, 321, 330, 421, and 440, with courses redesigned around AI-assisted programming, data, systems analysis, governance, and critical evaluation. University of Alabama MIS: How AI Is Transforming the Curriculum
Key Takeaways
- AI literacy training combines foundational knowledge, practical application, critical evaluation, and responsible AI use.
- Students, faculty, and administrators require different AI competencies based on their academic and institutional responsibilities.
- AI fluency develops as learners apply AI knowledge and judgment across different situations.
- Data literacy provides an important foundation for evaluating AI systems and their outputs.
- Institutions can integrate AI literacy through courses, existing curricula, and structured learning platforms across academic disciplines.
Frequently Asked Questions About AI Literacy Training
Is AI Literacy Training Required in College?
There is no universal requirement that every U.S. college student complete AI literacy training. Requirements vary by institution, program, and discipline, although accreditation and higher education organizations increasingly address emerging technologies and related competencies.
How Is AI Literacy Different From a Computer Science Course?
AI literacy focuses on understanding, evaluating, and responsibly using AI rather than building AI systems. Computer science typically provides deeper technical instruction in programming, algorithms, software development, and computing systems.
Do Faculty Need AI Literacy Training Too?
Yes. Faculty need AI literacy to make informed decisions about AI in teaching, assessment, research, academic integrity, and their disciplines while helping students use AI critically and responsibly.
Ready to Implement AI Literacy Across Your Institution?
Structured AI literacy training can help colleges and universities develop consistent competencies across students, faculty, programs, and disciplines while allowing individual departments to apply those skills within their own academic contexts.
What Is AI Literacy Training in Higher Education?
From the MIS Classroom to AI Engineering
For Sam Riddle, the path from the University of Alabama’s Management Information Systems program to developing technology at QuantHub happened alongside one of the biggest shifts the software industry has experienced in decades. After completing his bachelor’s degree and master’s degree in MIS at UA, Riddle joined QuantHub, where he now works as an AI engineer. His role is heavily rooted in software development: building web applications and writing production code that ultimately becomes part of the experience used by learners and educators on QuantHub’s platform.
Riddle’s relationship with QuantHub actually began before graduation. During his senior year, QuantHub contacted a professor Riddle worked for looking for a potential intern. The professor recommended him, and after a successful interview process, Riddle joined the company during his final semester in the MIS program. His internship included work in business, security auditing, and eventually development, giving him an early opportunity to apply what he had learned at UA in a professional environment.
That transition also came at an unusual moment in technology. When Riddle took his first MIS course in spring 2022, learning to program was still an overwhelmingly manual process. Students worked through loops, conditionals, algorithms, software installation, and other programming fundamentals themselves. Then generative AI arrived. Within roughly a year, students went from learning entirely by hand to experimenting with tools that could generate code from a prompt. That timing gave Riddle an unusual perspective: he learned the foundations of programming first and then experienced firsthand how AI could change the way those foundations were applied.
Learning to Code Before Learning to Code With AI
Rather than treating generative AI simply as something students should avoid, Riddle remembers the MIS program being open to responsible experimentation. Students still needed to understand their coursework and demonstrate their own knowledge, but they were also able to explore how AI could enhance the development process. As the technology improved, Riddle began using it more extensively. That experimentation eventually contributed to an AI-native application that he and fellow student Elliot helped build to win a hackathon. For Riddle, the combination proved valuable: the program first taught them how to code and then gave them room to learn how AI could augment those skills.
His capstone project pushed those abilities even further. Riddle’s team developed a prototype inspired by geographic information systems that took satellite imagery and displayed it on a map. The project presented problems that went far beyond simply knowing programming syntax. The students had to grapple with unfamiliar concepts such as satellite imagery formats and map projections while building a complex application. AI became a tool for navigating that unfamiliar territory, helping the team build something they did not initially possess all the specialized knowledge to create.
That experience foreshadowed an important reality of Riddle’s professional work: developers do not need to enter every project already knowing everything. What matters is having enough foundational knowledge to learn, evaluate, troubleshoot, and make decisions as new problems emerge. Today, Riddle has also had opportunities to partner with the MIS program as it considers how to teach students skills that developers are actively applying in the workplace. He sees educational gaps as inevitable—the technology simply changes too quickly for any curriculum to cover everything—but believes schools can shorten those gaps by encouraging exploration while teaching the fundamentals students need to continue learning independently.
What Tomorrow’s Developers Need to Know
For students preparing to enter development, Riddle’s advice is less about mastering one programming language and more about developing durable skills. AI can increasingly generate code in different languages, making syntax alone a less meaningful differentiator. Communication, critical thinking, reasoning, and collaboration, however, remain difficult to replace. Those abilities help developers understand a problem, communicate what needs to be built, assess the quality of an AI-generated solution, and work effectively with the people around them.
Technical practice still matters. Riddle points to habits such as continually building projects and pushing code as useful ways for aspiring developers to demonstrate that they are actively applying their skills. But underneath those technical habits is something even more fundamental: a willingness to learn. Knowledge can become outdated as tools and technologies change. The more important question, in Riddle’s view, is whether someone is willing to continually rebuild and expand that knowledge. The developers positioned to succeed will not necessarily be those who memorized the most at one moment in time, but those who are prepared to keep learning as the profession evolves.
Riddle recommends bringing the same mindset to AI. Developers should experiment with new technology without blindly adopting every new tool. In his own workflow, he thinks about AI as particularly strong at prediction and generation. That allows him to spend less time manually writing every line and more time thinking about what needs to be built and evaluating the result. His process can be summarized as exploration, planning, generation, and review: understand the problem, make a plan, allow AI to help produce the solution, and then put human eyes on the output before considering the work complete.
AI Will Change Development—But Human Judgment Still Matters
The rapid improvement of AI has understandably raised questions about what happens to software development jobs as these systems become more autonomous. Riddle sees the change firsthand. AI has already accelerated his development process and increased his output significantly. Yet greater productivity does not mean he is ready to hand the entire development process over to an autonomous agent. From his experience, AI can generate large amounts of code quickly, but without appropriate human oversight, the quality and direction of that work can suffer.
That distinction is important. A difficult development problem cannot always be solved simply by “throwing AI at it.” Developers need to understand where a tool performs well, where it struggles, what rules or context it needs, and which parts of a workflow are worth automating. Riddle expects AI to continue improving dramatically, but he views it primarily as an accelerator rather than a wholesale replacement for human work. His own job title may change as the industry evolves, but he is not particularly worried about development disappearing. Instead, he expects the profession itself to look different.
One reason is that even as AI’s technical mistakes become less noticeable, Riddle still sees what he describes as “directional” mistakes. A system can produce technically impressive output while misunderstanding what the developer actually wants. Planning, context, prompting, and human review therefore remain essential. The goal is not to keep a person involved in every repetitive task simply for the sake of doing so. It is to maintain the right amount of human involvement so automation saves time without sacrificing the quality or direction of the finished product.
The Skills That Should Never Be Automated Away
Perhaps the most significant lesson Riddle has taken from his time at QuantHub has little to do with code. Looking back on his experience as a full-time developer, he points to interpersonal moments—the Slack messages, code-review comments, conversations, calls, lunches, and team interactions—as some of the most valuable parts of professional development. AI can help automate pieces of those workflows, but completely automating them risks removing the collaboration that allows developers to learn from one another and makes working on a team rewarding in the first place.
That is why Riddle continues emphasizing soft skills when he speaks with students in the MIS program. His experience at QuantHub has reinforced the value of pushing beyond his own comfort zone to talk with coworkers, participate in team activities, and build relationships. Small actions may not seem as significant as learning a new technology or shipping a major feature, but over time they compound. For students preparing for their first development role, success can come from consistently doing those small things well: staying curious, communicating with others, learning continuously, and making deliberate decisions about when and how to use AI.
As AI reshapes development, Riddle’s experience suggests that the future may depend on an interesting combination of increasingly sophisticated technology and fundamentally human skills. Developers will write code differently. Their tools will become faster and more capable, and the knowledge they need will continually evolve. But judgment, curiosity, communication, collaboration, and the willingness to keep learning are unlikely to lose their value. As Riddle puts it, “we’re all just people.” For the next generation of developers, mastering AI will matter—but so will remembering how to do the human parts of the job well.
Prepare Students for the Future of AI-Powered Careers
Sam Riddle’s journey from MIS student to AI engineer demonstrates what’s possible when students develop strong technical foundations alongside the critical thinking, communication, and adaptability needed in a rapidly changing workplace. As AI continues to reshape software development and other data-driven careers, higher education programs have an opportunity to prepare students not just to use emerging technology, but to understand it, evaluate it, and apply it effectively.
QuantHub helps colleges and universities bring these skills into the classroom with curriculum designed for MIS and other higher education programs. Give your students opportunities to build practical AI and data skills while developing the human judgment and problem-solving abilities they’ll need to navigate an evolving workforce.
Explore QuantHub’s curriculum for MIS and higher education programs and discover how you can prepare your students for what’s next.
From MIS Student to AI Engineer: Sam Riddle’s Journey at QuantHub
The AI Conversation in Higher Education Is Changing
For universities, the conversation around artificial intelligence is quickly moving beyond whether students should be allowed to use it. The more important question is becoming whether institutions are adequately preparing students to use AI once they enter the workforce. That question was at the center of a recent QuantHub conversation bringing together information systems faculty and academic leaders to discuss what AI means for students, instructors, and the future of higher education.
Hosted by QuantHub Director of Marketing Kellie Weed and led by Director of Partnerships Jacob Krause, the conversation included perspectives from faculty and academic leaders at the University of North Carolina Greensboro, North Carolina A&T State University, and Tennessee State University. The goal wasn’t to present a single solution. Instead, faculty were invited to talk openly about what they are seeing in their classrooms, where students are struggling, and how institutions can learn from one another as AI becomes a more permanent part of education and work.
One of the first questions raised was whether the greater risk is students using AI too much or students not learning to use it at all. The faculty responses revealed that the issue isn’t quite that simple. Students need opportunities to work with AI, but they also need to understand what they’re doing, why they’re doing it, and when AI is or isn’t appropriate. The challenge for higher education is finding that balance.
Moving From AI Access to AI Fluency
Dr. Anita McCoy of UNC Greensboro argued that avoiding AI altogether may ultimately present the greater risk. Universities are preparing students to enter careers where AI is increasingly present, and sending graduates into that environment without experience using these tools could leave them at a disadvantage. At the same time, simply giving students access to AI isn’t enough. They can still use it poorly, misunderstand its capabilities, or accept its output without thinking critically about the result.
Dr. Lakshmi Iyer, Professor and Head of the Department of Information Systems and Supply Chain Management at UNC Greensboro, described a more purposeful approach. Rather than allowing students to use AI simply to arrive at an answer, faculty can connect AI activities directly to learning objectives. Students should still be expected to reason, reflect, explain their decisions, and demonstrate that they understand the underlying problem. AI can contribute to that process, but it shouldn’t replace it.
That distinction becomes particularly important when students leave the classroom. In a professional setting, being able to produce an AI-generated answer is very different from being able to evaluate, defend, and apply it. A student who has learned to treat AI as an easy button may struggle when an employer expects them to explain their reasoning. AI fluency, then, isn’t simply about knowing which tool to open or what prompt to type. It’s about knowing how to work with AI while remaining responsible for the outcome.
Faculty Are Learning Alongside Their Students
Universities have also had to evolve their own thinking. When generative AI first became widely accessible, much of the educational conversation centered on academic integrity. Faculty and administrators understandably wanted to know what AI meant for assignments, assessment, and cheating. But as the technology became more embedded in professional life, the discussion began to expand. The question was no longer only how to prevent inappropriate AI use, but how to teach appropriate use.
The faculty participating in the conversation described different ways their institutions are responding. Some have developed courses specifically focused on AI, while faculty are also being encouraged to integrate relevant AI tools into existing courses. Rather than requiring everyone to use a single platform, instructors may have room to experiment and determine which technologies make sense for their content and learning objectives. That flexibility is especially valuable while AI tools and capabilities continue to change rapidly.
Adoption among faculty remains a work in progress. One academic leader described a majority of faculty in his department as already experimenting with some degree of AI integration. Complete adoption may not be realistic, nor does every course necessarily require the same approach. What matters is creating an environment where faculty can explore the technology thoughtfully, compare experiences, and develop practices that preserve learning while preparing students for what they will encounter outside the university.
Students Are Starting From Very Different Places
One of the biggest complications for educators is that there is no single level of student AI readiness. Dr. Iyer described seeing a wide spectrum of abilities. Some students are already developing bots and participating in sophisticated AI projects. Others are still at the stage of entering an assignment into an AI system and essentially asking it to help them get a good grade. Both students may technically be “using AI,” but their level of understanding is dramatically different.
That gap could become even more pronounced as younger students enter college. Dr. McCoy compared AI adoption to the way people learned to use smartphones: many young people will simply pick up the technology and begin experimenting with it. Future students may arrive on campus having used AI throughout high school or even earlier. Familiarity, however, shouldn’t be confused with fluency. Someone can use a smartphone every day without understanding much about how the technology works, and the same can be true of AI.
This creates an important opportunity for educators. Instead of assuming students understand AI because they have used ChatGPT or another generative tool, institutions can provide structure around that experience. Students can learn about the benefits and limitations of AI from the beginning, develop better habits, and understand the responsibility that comes with using AI-generated information. As one participant put it, students should learn how to use AI properly whenever they begin using it, regardless of their age or academic level.
Preparing Graduates to Work With AI
Ultimately, the conversation returned to the reason universities are wrestling with these questions in the first place: students are preparing to enter a workforce that is changing. AI is increasingly being used for analytics, automation, communication, and other business functions. It is no longer a technology relevant only to developers or highly technical roles. The discussion framed AI fluency as an emerging foundational literacy that graduates across disciplines will increasingly need.
That reality naturally creates anxiety about jobs. Students see headlines about automation and may wonder whether the careers they’re preparing for will still exist. Faculty in the discussion emphasized a different way of looking at the change. Instead of teaching students to fear AI, universities can help them understand how their chosen professions are evolving and identify where AI can complement their expertise. Dr. Iyer summarized the idea particularly well: the greater competitive threat may not be AI itself, but another person who knows how to use AI effectively.
The challenge for higher education, therefore, isn’t to choose between embracing AI and protecting traditional learning. It is to do both. Students still need critical thinking, subject-matter expertise, reasoning, communication, and the ability to explain their work. Increasingly, they also need to understand how to apply AI without surrendering those abilities to it. Universities that can bring those pieces together will be better positioned to prepare graduates not simply to enter an AI-influenced workforce, but to participate in shaping what that workforce becomes.
Discover How QuantHub Can Transform Your MIS Curriculum
Discover how QuantHub helps universities integrate AI, programming, data analytics, and business skills into existing MIS programs—equipping students with the knowledge employers expect in an AI-driven workforce.
What AI Means for the Future of MIS Education
Connecting Alabama Students with Meaningful Careers in Data and AI
On July 31, the Alabama Data Scholars (ADS) program concluded another successful summer by celebrating the accomplishments of its 2026 cohort during the Alabama Data Scholars Showcase. The event marked the culmination of an eight-week paid internship program designed to connect Alabama high school and undergraduate students with meaningful work-based learning opportunities in data, artificial intelligence, analytics, and emerging technologies. More than a showcase of final projects, the event demonstrated how students can apply classroom learning to solve real business and community challenges across the state.
Powered by QuantHub and made possible through Innovate Alabama, the Alabama Data Scholars program continues to strengthen Alabama’s workforce pipeline by creating opportunities for students to gain professional experience before entering the workforce. This summer, 52 Alabama Data Scholars worked alongside mentors at 23 employer partners representing industries ranging from healthcare and finance to legal services, engineering, nonprofits, economic development, manufacturing, and technology. Throughout the summer, students paired technical learning through QuantHub’s AI curriculum with hands-on projects that addressed real organizational needs.
What makes Alabama Data Scholars unique is the breadth of experiences available to students. Rather than completing hypothetical classroom exercises, interns spent the summer designing AI workflows, building dashboards, automating business processes, developing applications, conducting market research, creating data visualizations, engineering prompts for large language models, and solving operational challenges for employers across Alabama. Their Showcase presentations highlighted not only technical achievement but also the professional communication, collaboration, and critical thinking skills that employers increasingly value.
AI, Automation, and Business Innovation in Action
Many projects demonstrated how students are already using AI to improve organizational efficiency. At Vivosphere, Evan Wang designed an autonomous competitive intelligence workflow that automatically monitored competitor websites and Google News using BrowseAI, Make.com, large language models, Google Sheets, and Outlook. The system filters relevant updates, organizes findings, and delivers concise reports automatically—giving the company an efficient way to stay informed while dramatically reducing manual research.
At Joey James Law Firm, Ethan Elliott explored one of AI’s fastest-growing professional disciplines: prompt engineering. His internship focused on researching, testing, and documenting prompting techniques that improve the reliability of locally deployed AI models used in legal workflows. By comparing prompting strategies, reducing hallucinations, and creating documentation for future implementation, Ethan demonstrated how responsible AI adoption depends as much on thoughtful design as it does on the underlying technology.
Another standout example came from Waypoint Investments, where Alabama Data Scholars Jai Varikuti and Sai Vajaha helped create practical AI solutions for a network of moving companies. Their projects included MoveShare, an automated system that analyzes transportation routes to identify profitable backhaul opportunities and generates daily recommendation reports, as well as an AI Innovation Hub that teaches employees how to effectively integrate AI into their daily work. Their work showcased automation, Python development, cloud technologies, prompt engineering, documentation, and human-centered AI adoption—all while producing tools the company continues to use after the internship ended.
Solving Real Business Challenges Through Data
Several scholars focused on transforming complex business data into actionable insights. At Altec, Dallas Lewis conducted a nationwide sales analytics research study by interviewing account managers, analyzing business intelligence applications, and organizing qualitative data into structured findings that leadership can use when evaluating future analytics tools. His work combined business intelligence, data analysis, research methodology, and stakeholder communication to support strategic decision-making.
Dianna-Grace Nelson, interning with TMB Tax & Financial Services, tackled a common business challenge: fragmented data. She developed a custom Python ETL pipeline that automated data ingestion and cleaning while building a scoring engine that linked information across more than ten survey modules. Her solution automatically identified engagement trends, highlighted missing milestones, and prioritized clients requiring follow-up, replacing hours of manual spreadsheet work with scalable automation.
At Three Notch Group, Ansley Watson combined Python, SQL, Streamlit, and AI-assisted development to build operational dashboards supporting engineering projects while also helping automate financial reporting systems. By creating ticket management workflows and interactive dashboards that update automatically alongside Excel data, she demonstrated how AI and software development can streamline engineering and financial operations while maintaining data accuracy and usability.
Technology Creating Community Impact
Many internship projects focused not only on business efficiency but also on improving communities throughout Alabama. At United Way of Madison County, Itimu Buchanan helped develop the Live United mobile application, creating a centralized platform where residents can access community resources, prescription savings programs, volunteer opportunities, and donation initiatives. Through the project, she strengthened her skills in data visualization, responsible AI, customer validation, and product development while helping expand access to critical services.
Another inspiring example came from Forge Breast Cancer Survivor Center, where an Alabama Data Scholar researched and designed an AI chatbot concept to help breast cancer survivors, caregivers, and families quickly access transportation assistance, financial resources, educational materials, and mental health support. The project emphasized privacy, empathy, trust, and responsible AI design, demonstrating how technology can enhance—not replace—human-centered care.
Creativity and innovation also took center stage at LunarLab, where Cameron Fleming transformed a business idea into a fully designed mobile application experience. Through extensive market research, competitor analysis, wireframing, and user experience design, Cameron developed an AI-powered food management application featuring meal planning, expiration tracking, savings dashboards, family sharing, and grocery intelligence. The project illustrated how entrepreneurship, design thinking, and AI can come together to solve everyday problems.
Building Alabama’s Future Workforce
While every Showcase presentation reflected a different project, one theme remained constant: students left the program with greater confidence, stronger technical skills, and a clearer vision for their future careers. Many participants shared that the internship confirmed their interest in fields such as artificial intelligence, software engineering, finance, business analytics, cybersecurity, healthcare technology, and entrepreneurship. Just as importantly, they developed professional communication, teamwork, leadership, and problem-solving skills that will serve them throughout their careers.
The impact extends beyond the students themselves. Employer partners gained fresh perspectives, innovative solutions, and meaningful contributions to ongoing projects while helping cultivate Alabama’s future workforce. Programs like Alabama Data Scholars demonstrate that investing in students today creates stronger organizations tomorrow, giving employers access to emerging talent while helping students envision long-term careers within the state.
As the 2026 Alabama Data Scholars cohort concludes, excitement is already building for next summer. QuantHub, Innovate Alabama, and program sponsors—including RxBenefits and The University of Alabama in Huntsville—hope to expand opportunities by welcoming even more employer partners and serving more students across Alabama. The success of this year’s Showcase made one thing abundantly clear: Alabama’s next generation of AI and data leaders is already making an impact, and the future of innovation in the state has never looked brighter.
Make an Impact on Alabama’s Next Generation
As Alabama Data Scholars continues to grow, so does the opportunity to shape the future of Alabama’s workforce. Whether you’re an employer looking to develop tomorrow’s AI and data talent, an educator interested in expanding career pathways for your students, or a student eager to gain hands-on xperience solving real-world challenges, there’s a place for you in the program.
Learn more about the Alabama Data Scholars initiative, explore partnership opportunities, or apply opportunities by visiting: quanthub.com/intern/
Building off the success of the Data Scholars summer program, QuantHub is also partnering with the Alabama Office of Civic Engagement to expand internships into the fall. Encourage HBCU students to apply for the inaugural fall AI for Alabama internship cohort at quanthub.com/intern.
Together, we’re building the next generation of innovators who will power Alabama’s future.
Thanks to our Program Sponsors and Partners
On behalf of QuantHub and the Alabama Data Scholars program, we’d like to extend our sincere thanks to our sponsoring organizations, Birmingham Business Alliance, Innovate Alabama, RxBenefits, and the University of Alabama in Huntsville, whose support helped make these incredible opportunities possible.
We’d also like to thank the 26 partner organizations that hosted projects and provided mentorship to interns throughout the 2026 summer program. Their time, expertise, and commitment gave students the opportunity to gain meaningful, real-world experience.
Alabama Data Scholars Showcase Celebrates Alabama’s Next Generation of AI and Data Talent
Choosing an AI and data literacy platform in 2026 means sorting through university MOOC providers, developer-focused course libraries, enterprise upskilling suites, and single-purpose AI collaboration tools. Each solves a different problem, and the right fit depends on whether you’re training a whole workforce, a technical team, or a classroom. This guide breaks down the nine platforms shaping the category, what each does best, and where each falls short for organizations that need AI and data fluency across every role, not just the technical ones.
1. QuantHub
The hook: QuantHub is an AI-powered micro-learning platform built to give an entire workforce or student body measurable AI and data fluency in five to ten minutes a day, not just the technical teams.
Core audience: Enterprise workforces, K-12 and higher ed institutions, non-technical professionals.
Delivery model: Adaptive micro-learning, personalized to each learner’s baseline and updated in real time.
Why it’s a top contender: QuantHub treats AI fluency as a baseline workplace skill, similar to email or spreadsheets, rather than a specialist capability reserved for engineers. Its adaptive engine assesses a learner’s starting point, then builds a personalized path covering AI fundamentals, data visualization, analytics, and machine learning, no coding background required. The platform is LTI 1.3 compliant, integrates with Canvas and PowerSchool, and has scaled statewide across Alabama public schools from an initial 16-school pilot. An independent evaluation of QuantHub Upskill met ESSA Level III “Promising Evidence” standards, finding statistically significant, positive links between usage and student engagement and career readiness. QuantHub also runs the Alabama Data Scholars program with Innovate Alabama, placing student interns with employers. QuantHub Upskill tracks progress org-wide and can go live within weeks; pricing is custom-quoted.
Where others fall short by comparison: Most competitors here were built for a narrower audience: developers, university students, or laboratory scientists. QuantHub was built for everyone in an organization or institution, with completion certificates and independently evaluated outcomes to prove it.
2. Pluralsight
The hook: Pluralsight is a technology skills platform for developers, cloud architects, and IT teams that recently launched a structured enterprise-wide AI literacy track.
Core audience: Software engineers and IT teams, plus broader business teams via AI Academy.
Delivery model: On-demand courses, hands-on labs, skill assessments, and live seminars.
Why it’s a top contender: Pluralsight has spent over a decade building deep technical curriculum in cloud computing, cybersecurity, and software development, and its skill assessments are well regarded among engineering teams. In March 2026, it launched Pluralsight AI Academy, a three-level program (AI Literacy, AI Productivity, Agentic AI) that scales from 500 to 100,000 participants and targets “every employee, from executives to frontline practitioners.”
Where it falls short by comparison: Pluralsight’s brand and library are still built around technical skill-building for engineers, and AI Academy is a recent addition layered on top rather than a platform built for non-technical, organization-wide fluency from the ground up.
3. DataCamp
The hook: DataCamp is an interactive coding and data skills platform for data scientists and analysts, now expanding into AI literacy for non-technical roles like marketing and finance.
Core audience: Data analysts and scientists, plus functional business teams.
Delivery model: Interactive, in-browser coding exercises in Python, R, SQL, and BI tools.
Why it’s a top contender: DataCamp is used by 80% of the Fortune 1000, and its hands-on coding sandboxes remain one of the most effective ways to build practical data skills. Its 2026 roadmap adds AI courses aimed specifically at non-technical, functional roles, reflecting the same market shift QuantHub was built around.
Where it falls short by comparison: DataCamp’s brand and interface are rooted in code-first learning. Learners uncomfortable with a coding environment may find it intimidating for a true company-wide rollout, next to QuantHub’s baseline-first, no-code approach.
4. Coursera
The hook: Coursera is a massive university-partnered course marketplace with growing AI-powered coaching features, now merging with Udemy to become one of the largest players in online learning.
Core audience: Individual learners and enterprise teams via Coursera for Business.
Delivery model: Video-based courses and Specializations, supported by an AI coaching assistant.
Why it’s a top contender: Coursera closed its acquisition of Udemy in May 2026, combining two of the largest course marketplaces in the category. Its AI coaching tool, Coursera Coach, is built into most courses, and its “AI Literacy for Everyone” Specialization is a credible entry point for non-technical learners.
Where it falls short by comparison: Coursera’s model centers on discrete courses a learner chooses individually, rather than an adaptive system that assesses a workforce’s skill gaps and builds a coordinated path across roles. Coursera also just announced post-merger layoffs, a reminder that consolidation can mean shifting priorities mid-contract.
5. Udacity
The hook: Udacity is a project-based tech credentialing platform, now owned by Accenture, best known for its Nanodegrees and a newly launched accredited AI Product Management MBA.
Core audience: Career-changers pursuing a portfolio-based tech credential.
Delivery model: Mentor-supported Nanodegrees with pre-defined projects, plus new accredited degrees.
Why it’s a top contender: Udacity’s project-based model, built with employer partners like Google, AWS, and NVIDIA, gives learners a tangible portfolio piece, and its acquisition by Accenture brought new resources and a first accredited degree program.
Where it falls short by comparison: Udacity’s catalog is narrower and more technical than most competitors here, with no free tier for full programs and no cohort or peer community. It fits individual career pivots into tech roles, not training a broad, mixed-skill workforce at once.
6. Ziplines Education
The hook: Ziplines Education partners with more than 30 universities to deliver cohort-based, practitioner-led workforce certificates in AI, digital marketing, and business analytics.
Core audience: Working professionals enrolling through a university continuing-education program.
Delivery model: Cohort-based, five-to-ten-week courses with embedded credentials from HubSpot, Tableau, and Google.
Why it’s a top contender: Ziplines reports an 80% completion rate and has built a genuinely useful bridge between universities and employers, with partnerships spanning the University of Arkansas, LSU, and Purdue. Its short-format, cohort-based structure suits learners who want a fixed timeline.
Where it falls short by comparison: Ziplines’ model is linear and cohort-scheduled rather than adaptive, operating through a revenue-share partnership with a university rather than as a standalone platform. It suits a defined certificate program, not continuous, organization-wide tracking.
7. BoodleBox
The hook: BoodleBox is a secure, FERPA-compliant hub that gives students and faculty unified access to multiple AI models like ChatGPT, Claude, and Gemini in one collaborative interface.
Core audience: Higher education institutions, with a growing footprint in corporate and workforce training.
Delivery model: A multi-model AI access and collaboration workspace with custom bot-building.
Why it’s a top contender: BoodleBox has scaled quickly, reaching more than 1,200 higher education institutions and raising a $5 million seed round in December 2025. Its token-reduction technology and compliance certifications (FERPA, SOC 2, HIPAA) make it a cost-effective way to give a campus community access to premium AI tools.
Where it falls short by comparison: BoodleBox is fundamentally an AI access and collaboration tool, not a structured skills curriculum. It gives learners a secure place to work with AI, but doesn’t assess baseline fluency or build a personalized path to close skill gaps the way a dedicated training platform does.
8. JoVE (Journal of Visualized Experiments)
The hook: JoVE is a peer-reviewed scientific video journal and education library used by STEM laboratories and university science programs worldwide.
Core audience: Researchers, lab scientists, and STEM instructors.
Delivery model: A subscription video library of peer-reviewed methods, plus curriculum-focused video collections.
Why it’s a top contender: JoVE is indexed in PubMed and Web of Science, and its video-first format solves a real problem: complex lab techniques are easier to learn and replicate on video than through text alone. It’s a trusted resource across thousands of university science departments.
Where it falls short by comparison: JoVE is purpose-built for laboratory science, with no application to enterprise AI fluency or non-technical workforce training. It’s a niche academic resource, not a broad competitor to a platform like QuantHub.
9. 2U / edX
The hook: 2U and edX operate as a combined online learning platform, offering university-branded MOOCs, professional certificates, and full degree programs through partnerships with more than 230 institutions.
Core audience: Learners seeking university-branded credentials, from free audits through full degrees.
Delivery model: Video-based MOOCs and degree programs, delivered through 2U’s technology and edX’s marketplace, which 2U has owned since acquiring it in 2021.
Why it’s a top contender: The combined platform reaches more than 40 million learners and includes programs from top-ranked universities like Harvard, MIT, and UC Berkeley. Its MicroMasters and certificate programs carry real weight where the issuing institution is well recognized.
Where it falls short by comparison: 2U underwent a Chapter 11 debt restructuring in 2023, and pricing and free-audit access have tightened since the platform moved from nonprofit to for-profit. Its programs are built around long-form academic learning rather than the fast, adaptive updates the AI landscape demands.
Choosing the right platform for your organization
The right platform depends on who needs training and how quickly you need results. Pluralsight and DataCamp serve technical teams well. Coursera, 2U/edX, and Ziplines Education fit individual credentialing tied to a university. BoodleBox solves AI access, not skill-building. JoVE serves a scientific niche. Udacity fits individual technical career pivots.
For organizations that need AI and data fluency across every role, from the C-suite to the frontline, in weeks rather than semesters, QuantHub is the platform built specifically for that job. Book a demo to see how QuantHub can build a tailored rollout for your team or institution.
Sources
- QuantHub, company homepage and platform overview, 2026: https://www.quanthub.com/
- Pluralsight, “Pluralsight Launches AI Academy to Help Enterprises Measure and Scale AI Productivity,” March 3, 2026: https://www.pluralsight.com/newsroom/press-releases/pluralsight-launches-ai-academy-to-help-enterprises-measure-and-0
- Pluralsight, “Pluralsight Launches New AI Sandbox, Guided Learning, and Enterprise Integrations,” April 15, 2026: https://www.pluralsight.com/newsroom/press-releases/-pluralsight-launches-new-ai-sandbox–guided-learning–and-enter
- DataCamp, “The State of Data & AI Literacy in 2026,” February 26, 2026: https://www.datacamp.com/blog/the-state-of-data-and-ai-literacy-in-2026-definitions-statistics-and-the-ai-skills-gap
- DataCamp for Business, platform and Fortune 1000 usage data, 2026: https://www.datacamp.com/business
- Coursera Blog, “Empowering leaders to build a skills-first future,” March 11, 2026: https://blog.coursera.org/a-new-era-of-learning/
- GuruFocus, “Coursera Plans Layoffs Following Udemy Acquisition,” July 6, 2026: https://www.gurufocus.com/news/8946251/coursera-plans-layoffs-following-udemy-acquisition
- IntuitionLabs, “Coursera Udemy Merger: An Analysis of the Online Learning Market,” April 18, 2026: https://intuitionlabs.ai/articles/coursera-udemy-merger-analysis
- SkillsCouter, “Udacity Review 2026: Is a Nanodegree Worth It?,” 2026: https://skillscouter.com/udacity-nanodegree-review/
- Arkansas Business, “University of Arkansas Expands Workforce Education as AI Reshapes Jobs,” 2026: https://www.arkansasbusiness.com/article/university-arkansas-partners-ziplines-education-offer-fast-track-tech-training/
- University of Arkansas News, “U of A and Ziplines Education Offer Core Workforce Skills via Online Training,” May 27, 2026: https://news.uark.edu/articles/82349/u-of-a-and-ziplines-education-offer-core-workforce-skills-via-online-training
- Council of Independent Colleges, BoodleBox partnership overview, updated March 13, 2026: https://cic.edu/resource/boodlebox/
- PR Newswire / BoodleBox, “BoodleBox Secures $5 Million in Funding to Accelerate AI Collaboration in Higher Education,” December 10, 2025: https://www.prnewswire.com/news-releases/boodlebox-secures-5-million-in-funding-to-accelerate-ai-collaboration-in-higher-education-302637377.html
- Wikipedia, “JoVE,” last revised 2026 (2024 impact factor data): https://en.wikipedia.org/wiki/JoVE
- Course Careers, “edX Review 2026: Free Courses, Certificates & Costs,” March 5, 2026: https://course.careers/blog/best-edx-courses
- Wikipedia, “2U (company),” last revised June 6, 2026: https://en.wikipedia.org/wiki/2U_(company)
All competitor facts above were independently verified via live web search in July 2026 and cross-checked against each company’s own site or a 2026-dated third-party source. None were taken solely from AI-generated summaries.
- QuantHub ESSA Level III evidence brief (source for the “Promising Evidence” claim): https://www.quanthub.com/wp-content/uploads/Ed-Tech-Collective-QuantHub-Earns-ESSA-LEVEL-III.pdf
- Innovate Alabama, Alabama Data Scholars program page: https://innovatealabama.org/programs/quanthub-alabama-data-scholars/