{"id":78807,"date":"2025-11-04T23:02:15","date_gmt":"2025-11-05T04:02:15","guid":{"rendered":"https:\/\/world.edu\/?p=78807"},"modified":"2025-11-04T23:02:15","modified_gmt":"2025-11-05T04:02:15","slug":"how-ai-is-challenging-the-credibility-of-some-online-courses","status":"publish","type":"post","link":"https:\/\/world.edu\/how-ai-is-challenging-the-credibility-of-some-online-courses\/","title":{"rendered":"How AI is challenging the credibility of some online courses"},"content":{"rendered":"

\"\"<\/p>\n

Distance learning far precedes the digital age. Before online courses, people relied on print materials (and later radio and other technologies)\u00a0to support formal education when the teacher and learner were physically separated<\/a>.<\/p>\n

Today, there are varied ways of supporting distance learning with digital communication. With \u201casynchronous\u201d online courses, teaching does not occur live. Students access course materials on the learning management system and complete assignments at their own pace. This allows flexibility across time zones and work schedules and\u00a0affords accessible learning<\/a>.<\/p>\n

Nevertheless, some researchers have raised concerns regarding the\u00a0quality<\/a>\u00a0and\u00a0student outcomes<\/a>\u00a0associated with asynchronous online courses. As well, generative artificial intelligence (GenAI) has exposed\u00a0fundamental challenges<\/a>\u00a0to this mode of delivery.<\/p>\n

While\u00a0GenAI poses serious challenges to academic integrity in many formats of learning<\/a>, including synchronous online and in-person learning, asynchronous courses face the most acute risk. Without real-time interaction or time constraints, students can use AI undetected while instructors never observe their thinking processes.<\/p>\n

Compromised learning models<\/strong><\/h3>\n

Asynchronous courses have long relied on conventional assessments: discussion board posts, written reflections, essay assignments and pre-recorded videos. These models in asynchronous assessment are now compromised.\u00a0Distinguishing AI-generated<\/a>\u00a0content from human-written text has become increasingly difficult.<\/p>\n

Text discussions and reflections present the highest substitution risk. GenAI can generate personalized\u00a0reflective posts<\/a>\u00a0and discussion replies rapidly, complete with a polished academic tone. An instructor may spend hours responding to these contributions yet gain little evidence about who actually learned and produced the material.<\/p>\n

AI agents<\/a>\u00a0like\u00a0ChatGPT\u2019s Atlas browser<\/a>\u00a0can now\u00a0navigate course sites, consume materials and complete some assignments<\/a>\u00a0with minimal student intervention \u2014 if any.<\/p>\n

In written assignments, requiring precise citations from assigned course materials may seem like a safeguard. However, AI-enhanced tools can easily meet such requirements. This approach provides false security and fails to address\u00a0underlying problems<\/a>, including\u00a0authorship with integrity in an AI world<\/a>.<\/p>\n

Students can be asked to provide time-stamped drafts, version history and checkpoints to document their process. But these can be easily fabricated \u2014 while instructors become overloaded with policing rather than focusing on students\u2019 learning and progress.<\/p>\n

AI-generated\u00a0infographics<\/a>\u00a0and\u00a0videos<\/a>\u00a0are also becoming hard to distinguish from human-made ones.<\/p>\n

Detectors, remote proctoring not solutions<\/strong><\/h3>\n

AI detectors\u00a0cannot solve<\/a>\u00a0the problem.\u00a0Research suggests <\/a>\u00a0detection tools produce false positive rates far higher than advertised, with disproportionate harm to neurodivergent and second-language learners. Several universities now\u00a0explicitly advise against using detection software<\/a>\u00a0as evidence of academic misconduct.<\/p>\n

Remote proctoring<\/a>\u00a0is intrusive and raises serious ethical, equity, privacy and reliability concerns. Students requiring accommodations, whether for disabilities, inadequate technology or lack of private space, must be granted leniency that undermines the system\u2019s purpose, rendering it unsustainable while diverting instructors away from their educational mission.<\/p>\n

These mounting challenges are neither hypothetical nor distant. Without meaningful intervention, institutions risk credentialing students who have not demonstrably engaged with course content, thereby undermining the integrity of academic credentials.<\/p>\n

Two less-than-ideal strategies<\/strong><\/h3>\n

Genuine protection against AI substitution requires approaches that fundamentally alter how instructors deliver asynchronous courses. Two strategies that somewhat meet this threshold are:<\/p>\n

1)\u00a0Short oral examinations<\/strong>\u00a0can be scheduled for major assignments or throughout a term. While not without\u00a0limitations<\/a>, these conversations verify authorship and\u00a0assess the depth of understanding<\/a>.<\/p>\n

2)\u00a0Experiential learning components with external verification:<\/strong>\u00a0Students can apply course concepts to real-world settings and include brief attestations from workplace supervisors, community partners or other external stakeholders in their capstone assignments that will be graded by course instructors. Combined with short oral examinations, this approach would deter offloading all learning to AI and augment asynchronous coursework with practical components.<\/p>\n

However, assessment strategies alone cannot solve the authenticity crisis.<\/p>\n

Rethinking program design<\/strong><\/h3>\n

The\u00a0Community of Inquiry framework<\/a>, a tool\u00a0for conceptualizing online learning<\/a>, identifies three essential elements for effective online learning: social presence (students engage authentically), cognitive presence (students construct understanding through inquiry) and teaching presence (instructors facilitate learning).<\/p>\n

GenAI threatens all three of these elements: it can simulate social engagement through generated posts, substitute for cognitive work and force instructors to focus on policing rather than teaching.<\/p>\n

Institutions must evaluate whether their asynchronous programs can maintain these elements given GenAI capabilities.<\/p>\n

Confronting an uncomfortable reality<\/strong><\/h3>\n

Institutions and educators must be honest about limitations. Few strategies provide genuine protection against AI substitution; most merely create friction that determined students can overcome. The recommended approaches named above require synchronous elements or external verification that fundamentally alter asynchronous delivery.<\/p>\n

Implementation of these imperfect solutions requires genuine institutional commitment, resources and policy support. Institutions now face a choice: invest substantially in what is required to restore some degree of assessment authenticity or acknowledge that asynchronous programs (as currently structured) cannot credibly assure learning outcomes.<\/p>\n

Band-aid solutions and deflection of responsibility to instructors will only deepen the credentialing crisis. In the absence of robust institutional efforts, asynchronous programs risk becoming credential mills in all but name. The question is not whether institutions can afford to act, but whether they can afford not to.<\/p>\n

Author Bios:<\/strong> Mohammed Estaiteyeh is Assistant Professor of Digital Pedagogies and Technology Literacies, Faculty of Education and Rahul Kumar is an Assistant Professor, Faculty of Education both at Brock University<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Distance learning far precedes the digital age. Before online courses, people relied on print materials (and later radio and other technologies)\u00a0to support formal education when the teacher and learner were physically separated. Today, there are varied ways of supporting distance learning with digital communication. With \u201casynchronous\u201d online courses, teaching does not occur live. Students access […]\n… learn more→<\/a><\/p>","protected":false},"author":33,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[15645],"tags":[435566],"coauthors":[11733],"class_list":["post-78807","post","type-post","status-publish","format-standard","hentry","category-education","tag-asynchronous-online-courses"],"acf":[],"yoast_head":"\nHow AI is challenging the credibility of some online courses - World leading higher education information and services<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/world.edu\/how-ai-is-challenging-the-credibility-of-some-online-courses\/\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:locale\" content=\"en_US\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:type\" content=\"article\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:title\" content=\"How AI is challenging the credibility of some online courses - World leading higher education information and services\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:description\" content=\"Distance learning far precedes the digital age. Before online courses, people relied on print materials (and later radio and other technologies)\u00a0to support formal education when the teacher and learner were physically separated. Today, there are varied ways of supporting distance learning with digital communication. With \u201casynchronous\u201d online courses, teaching does not occur live. 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