01
将检测得分定位为辅助线索
AIGC 疑似度评分不应作为评判学术失信的唯一标准，而应作为发现和定位需要人工审阅、辅导或关怀段落的辅助线索。
多维度分析学生文稿的 AIGC 疑似特征。建议结合学生的写作大纲、笔记及日常表现进行综合评估，避免单凭百分比得分下结论。
Document input
仅影响检测规则，不会翻译正文。
检测要点
AIGC 检测面板
AI 特征预估
粘贴或上传文稿后开始检测，这里会显示分数与分句信号。
预检
01字数
0
02词数
0
03语言
简体中文
04来源
粘贴
指南
多维度分析学生文稿的 AIGC 疑似特征。建议结合学生的写作大纲、笔记及日常表现进行综合评估，避免单凭百分比得分下结论。
步骤 1
文稿特征扫描
步骤 2
比对历史习作
步骤 3
私下沟通与辅导
指南
将检测得分定位为辅助线索
防范潜在的算法误判
结合写作过程性证据
01
AIGC 疑似度评分不应作为评判学术失信的唯一标准，而应作为发现和定位需要人工审阅、辅导或关怀段落的辅助线索。
02
非母语者写作、高度程式化的学术报告，或者经过语法校对软件大幅修正润色的文本，均可能被算法判定为偏高的 AIGC 疑似特征。
03
建议结合学生的思维导图、文献卡片或历史修改版本。亲自撰写文稿的学生通常能够清晰地阐述其观点取舍与论证逻辑。
What we check
The teacher page is built around classroom review, where text length, assignment format, and student history all change how a detector result should be read.
Scan the full paper and inspect flagged passages instead of acting on one global percentage.
Keep citations and formal prose in context, because academic style often looks more predictable than casual writing.
Discussion posts and lab notes can be noisy. Short-text flags should be compared with classroom writing baselines.
Use the score as a review trigger before grading, then ask process questions where the signal is concentrated.
Teacher workflow
The classroom problem is not only whether a detector can find AI-like text. The real issue is whether the teacher can make a fair, defensible decision after the score appears.
Use TurnitPass when the school LMS score feels too thin or when you need a sentence-level review before acting.
Compare sentence rhythm, vocabulary, and argument structure with in-class writing or earlier submissions.
Use the result to ask better process questions, not to replace academic integrity judgment.
Review path
Cross-check the result
If only one system flags the work, treat the signal as weak. Agreement across tools is stronger but still not a verdict.
Review the flagged passages
Look for concentrated blocks in introductions, conclusions, and transition paragraphs rather than random isolated lines.
Ask for process evidence
Draft history, source notes, outlines, and a private explanation usually matter more than any single score.
False positives
False positives are part of every detector workflow. A fair review process keeps the score, the text, and the student conversation together.
If only one system flags the work, treat the signal as weak. Agreement across tools is stronger but still not a verdict.
Look for concentrated blocks in introductions, conclusions, and transition paragraphs rather than random isolated lines.
Draft history, source notes, outlines, and a private explanation usually matter more than any single score.
Compare the workflow around the score, not just the existence of a detector.
TurnitPass
Classroom signal plus revision context
Breakdown
Sentence and pattern guidance
Next step
False-positive workflow
Turnitin AI
Institutional bundled score
Breakdown
Limited visible evidence
Next step
Depends on school policy
GPTZero
Single detector classifier
Breakdown
Sentence details on some plans
Next step
Requires teacher process review
Classroom policy
Treat the scan as a review trigger. The useful workflow keeps the score, the flagged passage, the student process, and the final decision in the same record.
Policy
Define what AI use is allowed, disclosed, or prohibited before assignments begin.
Evidence
Pair detector estimates with drafts, notes, prior writing, and a private explanation.
Decision
Do not penalize from a score alone; document the full review context.
流程
Review record
01
Draft source
Full submission + class context
02
Detector signal
Clustered passages, not a verdict
03
Next action
Conversation or no action
Further reading
不能。疑似度得分只能作为辅助线索，必须与写作过程性证据及教师的专业学术判断相结合。
不会。系统仅提供即时文本扫描与分析，不保存任何学生名册或作业，全面保障学生隐私。