Maps That Mislead

Navigating College Information in the Digital Age

Editor’s Note: Maps That Mislead

Over the past weeks, we’ve explored what intervening in unjust systems means and what happens when those interventions fade. Intentional Interventions traced how small nudges and systemic shifts can help students navigate barriers not of their making. The Data Gatekeepers offered a deeper reckoning: Who controls the systems that determine which students matter—and which opportunities they see?

This week, we turn from the systems themselves to the students who move within them. Maps That Mislead invites us into the lived experience of Erica, a valedictorian from rural Washington whose aspirations are shaped not just by talent or determination, but by the digital tools she’s taught to trust. With a family that brings different forms of wisdom, she navigates these systems without the specific institutional knowledge these digital maps assume.

Erica’s story also echoes themes explored in Agency vs. Systems, where individual resolve meets the often invisible structures constraining possibility. Her journey parallels and contrasts with Jon’s, first introduced in Intentional Interventions, whose connection to a community-based organization helped him question the very systems that quietly narrowed Erica’s world.

From financial aid calculators to algorithmic recommendations, the digital maps students follow promise clarity but often conceal crucial paths. What appears on screen becomes the totality of possibility, unless you have college-going social capital: the kind of insider guidance that helps you question what’s not shown.

As you read, consider: What assumptions are built into the tools students rely on? How do we design guidance systems that expand rather than shrink their sense of what’s possible? And what would it take to create technologies that recognize, not flatten, the rich diversity of student experience?

Next week, we follow students like Arya as they step onto college campuses and confront a new set of maps and terrains in Strange Lands: Culture Shock on Campus.

~ Dr. G

Erica stares at her laptop screen, the blue light illuminating her face in the predawn darkness of her family’s rural Washington home. In the next room, her mother, who navigated her own educational journey when she immigrated as a teenager, prepares for her shift as a quality specialist at the regional fruit-packing facility, leaving a note of encouragement in their shared language. Three browser tabs compete for Erica’s attention: the FAFSA website with its complex, maze-like forms, a college recommendation algorithm suggesting schools she’s never heard of, and a financial aid calculator showing numbers that don’t align with her reality. Outside her window, the distant Cascade mountains are still shrouded in darkness, much like the path to college that lies before her. She dreams of becoming an engineer, building bridges that connect communities like hers to the wider world.

Despite being valedictorian of her small high school, Erica feels lost. The digital maps guiding her college journey promise completeness while concealing critical routes. Following each step the algorithm recommends, she steadily narrows her list. The bias is subtle: the recommendation engine highlights nearby institutions and omits others that her academic record could easily support. She assumes this must be the best fit. After all, it’s what the system suggests. Unlike a conversation with a counselor, these digital maps don’t invite dialogue or nuance. They present decisions as destinations: static lists, calculated estimates, and algorithmically sorted matches, leaving little room for context, creativity, or complexity.

When Maps Distort Reality

Digital information systems have fundamentally transformed how students navigate the path to higher education. While we explored who controls these systems last week, we now turn to how students experience them. For students like Erica, digital platforms don’t merely facilitate college searches. They actively shape understanding of what’s possible, appropriate, or attainable.

The problem isn’t just inadequate information but misleading information that appears authoritative. When algorithms and digital systems present themselves as objective guides, their embedded biases and limitations become invisible. Students don’t question the paths not shown or the opportunities filtered out, because the very nature of digital interfaces suggests completeness. What appears on screen becomes the totality of possibility.

From Personal Networks to Digital Platforms

College guidance has evolved dramatically. Historically, students relied on personal networks—family members with college experience, well-connected counselors, or community mentors. This system explicitly privileged those with college-going social capital while leaving others adrift.

Digital platforms promised to democratize college information, yet they often narrow rather than expand students’ visions of what’s possible. These tools might favor nearby or familiar schools for students like Erica, even when her academic achievements would likely qualify her for a wider range of opportunities. Search results and drop-down menus become de facto guidance, shaping options that appear viable and leaving others hidden from view. But informed choice requires visibility. Students can only dream of paths they’re allowed to see.

The FAFSA form, for instance, assumes family financial situations that align with traditional middle-class households. Questions about parent contributions and household assets become bewildering for students from non-traditional family structures or immigrant households where financial discussions remain taboo. The form’s design reflects assumptions about the “typical” college student and how their family operates.

The Illusion of Comprehensive Information

Perhaps the most insidious aspect of digital college guidance is its appearance of completeness. When Erica inputs her test scores, GPA, and interests into a college recommendation engine, she cannot know what factors the algorithm prioritizes or what biases might be encoded in its suggestions.

Financial aid complexity compounds this problem. Despite online calculators promising clarity, the maze-like system of grants, loans, and scholarships remains opaque. These digital tools often deliver confusion instead of illumination. Their interfaces foreground intimidating estimates while hiding the real picture behind multiple tabs or small-print disclaimers. Erica takes the number at face value, unaware that net price could shift dramatically with aid packages, merit scholarships, or institutional support she's never heard of. The visual hierarchy of these tools shapes perceptions of affordability more powerfully than the actual numbers.

These digital systems reflect and amplify existing biases through algorithmic recommendations that appear scientific but often reinforce historical patterns. While Jon, from previous essays, benefits from a community organization that helps him question these digital recommendations, Erica, like most students in her position, faces them alone, taking their authority at face value. Her family wants the best for her, but lacks the specialized knowledge to challenge the digital gatekeepers that shape her options. Her mother, whose own education was interrupted when she immigrated, offers steady support and guidance about persistence, even as they navigate forms designed for families unlike their own.

Following All the “Right” Digital Steps

Erica does everything by the book. She completes each FAFSA section meticulously, inputs accurate information into college search engines, and follows the step-by-step guides provided by her high school’s limited college counseling resources. She trusts the process completely.

The algorithm might see something in her record that makes her unfit for broader pathways. Erica assumes this explanation when she encounters digital barriers. When financial aid estimators present intimidating numbers, she further narrows her vision, unaware that these tools often fail to account for the full range of aid possibilities. Each digital interaction reinforces an increasingly constrained view of what's possible.

What Erica doesn’t realize is how these systems interpret her data. Her rural zip code, her family’s limited college history, and the fact that she helps care for her younger siblings all shape the recommendations in ways neither she nor her family can decipher. These systems weren’t designed to recognize how her part-time job at the local hardware store, where she helps farmers and builders solve real-world problems, demonstrates initiative, adaptability, and practical intelligence. They miss the strengths forged in her community, where distance shapes opportunity and resourcefulness is developed daily. Her math teacher insists she belongs at a top engineering program—but the system speaks louder. It’s a moment that echoes the tension we explored in Agency vs. System: how individual effort, no matter how extraordinary, can be misread or dismissed when the algorithms meant to guide opportunity inherit the blind spots of the systems they replaced.

By senior year’s end, Erica has committed to a local state university with limited programs in her field of interest. Despite her strong standardized test scores—metrics that still carry weight in many admissions processes—she doesn’t see the broader landscape of opportunity. Not because she lacked ambition, but because the digital maps she trusted narrowed her perceived options before she could fully explore them. While her story highlights the impact of information asymmetry on high-achieving students, these flawed systems also misguide students with lower grades or test scores, steering them away from pathways that could lead to success.

When Systems Fail: FAFSA Delays and Recommendation Flaws

The recent FAFSA modernization efforts provide a timely example of how digital systems can fail the students most dependent on them. Technical problems delayed processing for hundreds of thousands of students in 2023–2024, disproportionately affecting those without advisors who could help navigate the disruption.

AI college recommendation systems present similar problems. These algorithms typically train on historical application and enrollment data, perpetuating patterns where certain demographic groups attend certain types of institutions. When Erica receives recommendations biased toward regional colleges, she’s experiencing the algorithm’s prediction based on students with similar backgrounds, not necessarily what would be optimal for her unique talents and goals.

What’s particularly troubling is how these systems appear more legible to those already fluent in institutional logic. Students from college-educated families often approach algorithmic recommendations skeptically, understanding them as suggestions rather than mandates. They possess the contextual knowledge to question results that don’t align with their expectations. Without this institutional fluency, Erica sees the same recommendations as authoritative declarations of what’s possible.

Redrawing the Maps

Addressing these misleading maps requires more than technical fixes. It demands reimagining how information systems account for diverse student experiences and needs.

Alternative approaches to information design might include:

• Contextual explanations that help students understand how recommendations are generated

• Transparency about algorithm limitations and potential biases

• Multiple pathways presented rather than single “best fit” options

• Community-based validation systems where students from similar backgrounds can share experiences

• Explicit acknowledgment of data gaps for underrepresented students

Students navigating these systems should ask critical questions: What assumptions does this platform make about my background? What alternatives aren’t being shown? How might my unique circumstances affect these recommendations? Who designed this system, and what biases might they bring?

Greater transparency might dramatically change outcomes. If Erica could see that the algorithm tends to highlight nearby options more frequently than distant ones for students from similar backgrounds, she might question its authority. If she understood that net costs after institutional aid often make options she assumed were out of reach more affordable than their sticker prices suggest, sometimes even rivaling the net cost of her seemingly more accessible local options, her perceived options would expand. And beyond cost alone, few tools help students weigh the long-term value of access to networks—social capital that quietly shapes opportunity long after college ends.

As these digital maps increasingly determine educational pathways, we must ensure they reflect the full landscape of possibility rather than reinforcing familiar and limited routes. The question isn’t whether we use digital guidance systems, but whether these systems truly illuminate all possible paths, especially for those students most dependent on their guidance to find their way.

As always, many thanks for reading.

Dr. G