Establishing clear expectations for generative artificial intelligence (GAI) in your course is the first step in supporting student learning. Once you have established how students may or may not use GAI in your course, consider some of the ways you can ensure these expectations are being met. This resource can help you interpret student work and identify patterns that may indicate GAI use. <\/p>\n
It can be helpful to look for patterns in student work rather than relying on one specific signal. The indicators below are grouped by strength and should be considered in relation to the course context, the assignment, and the student\u2019s prior work. <\/p>\n
These features, if present, can strongly suggest that a submission was generated or heavily assisted by GAI. <\/p>\n
Some submissions contain phrasing that reflects how GAI tools \u201ctalk\u201d when responding to prompts. This may include language that positions the writer as an assistant rather than a student, such as: <\/p>\n
GAI tools are prone to fabricating or misusing sources. Concerning patterns may include: <\/p>\n
A submission may stand out if it closely mirrors GAI-generated text while differing noticeably from peers\u2019 work. This can include: <\/p>\n
These features may raise concern but are generally not sufficient on their own to determine whether GAI was used. <\/p>\n
Because GAI tools generate text based on patterns rather than understanding, they may present incorrect or invented information confidently. <\/p>\n
GAI-produced writing often lacks specificity. This can show up as: <\/p>\n
Some submissions may appear well written yet do not meaningfully respond to the assignment question or task, suggesting a mismatch between the prompt and the generated output. <\/p>\n
Some possible indicators include: <\/p>\n
When considering language-related indicators, instructors should take care not to conflate GAI use with linguistic differences or legitimate use of grammar support tools. <\/p>\n
Whenever possible, concerns about GenAI misuse should be grounded in comparison with the student\u2019s own prior work. <\/p>\n
When concerns arise about suspected unauthorized GAI use, instructors can invite the student to discuss their decision-making and their writing process in completing the assignment. Depending on the course context, it may also be appropriate to request drafts, notes, or reflective process statements if these elements were not built into the assessment design. <\/p>\n
Flags from approved detection tools may be considered as part of a broader review but should not be treated as definitive evidence on their own. <\/p>\n
Memorial\u2019s statement<\/a> on GAI provides guidance on the use of GAI detection tools as evidence to levy academic misconduct allegations. The use of GAI detection tools is not recommended,<\/em> and their results cannot be the sole basis for an academic misconduct allegation.<\/em> While some tools may be more accurate than others in their analysis, the results should not be considered definitive. <\/p>\n Establishing clear expectations for generative artificial intelligence (GAI) in your course is the first step in supporting student learning. Once you have established how students may or may not use GAI in your course, consider some of the ways you can ensure these expectations are being met. This resource can help you interpret student work and identify patterns that may indicate GAI […]<\/p>\n","protected":false},"author":255,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[225],"tags":[241,259],"collection":[227],"class_list":["post-22563","post","type-post","status-publish","format-standard","hentry","category-generative-ai","tag-academic-integrity","tag-ai","collection-generative-ai"],"featured_image_src":null,"author_info":{"display_name":"simon","author_link":"https:\/\/blog.citl.mun.ca\/instructionalresources\/author\/simon\/"},"acf":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p7y0Lp-5RV","_links":{"self":[{"href":"https:\/\/blog.citl.mun.ca\/instructionalresources\/wp-json\/wp\/v2\/posts\/22563","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.citl.mun.ca\/instructionalresources\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.citl.mun.ca\/instructionalresources\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.citl.mun.ca\/instructionalresources\/wp-json\/wp\/v2\/users\/255"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.citl.mun.ca\/instructionalresources\/wp-json\/wp\/v2\/comments?post=22563"}],"version-history":[{"count":3,"href":"https:\/\/blog.citl.mun.ca\/instructionalresources\/wp-json\/wp\/v2\/posts\/22563\/revisions"}],"predecessor-version":[{"id":22585,"href":"https:\/\/blog.citl.mun.ca\/instructionalresources\/wp-json\/wp\/v2\/posts\/22563\/revisions\/22585"}],"wp:attachment":[{"href":"https:\/\/blog.citl.mun.ca\/instructionalresources\/wp-json\/wp\/v2\/media?parent=22563"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.citl.mun.ca\/instructionalresources\/wp-json\/wp\/v2\/categories?post=22563"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.citl.mun.ca\/instructionalresources\/wp-json\/wp\/v2\/tags?post=22563"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/blog.citl.mun.ca\/instructionalresources\/wp-json\/wp\/v2\/collection?post=22563"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Challenges with GAI Detectors <\/h3>\n
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Resources<\/strong><\/h2>\n
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