public-release-20260715-005
与 public-release-20260714-002 相比。
Imperial College London public-release-20260715-005 差异
将 public-release-20260714-002 与 public-release-20260715-005 进行比较。
变更日志
Release-to-release tracker diff history with separate policy-text, newly-extracted claim, evidence, and source snapshot categories.
当前公共记录的新鲜度与审核状态。
Imperial College London currently has 14 source-backed claim records and 15 official source attributions. Latest tracked changed date: 2026年7月18日. Latest tracker diff: 0 comparable policy-text changes, 4 newly extracted claims, 1 source snapshot changes.
此页面汇总了所有针对的公开版本差异记录 Imperial College London。各个版本的发布快照仍可通过其特定发布的 URL 访问。
This tracker is not legal advice, not academic integrity advice, and not an official university statement unless a linked source is the university's own official page.
新提取的主张是追踪器的新增项,不一定是大学新发布的。来源快照变更显示了同一来源 URL 的哈希值变化,这本身并不代表政策发生了变化。
此版本差异的语义分类。
由该大学的所有公开发布快照生成的统一追踪器差异。
将 public-release-20260714-002 与 public-release-20260715-005 进行比较。
1 公开发布差异
与 public-release-20260714-002 相比。
将 public-release-20260714-002 与 public-release-20260715-005 进行比较。
14 声明 条记录
dAIsy is listed for Imperial College London in an official university AI tools source. Derived availability: allowed. Derived endorsement type: officially endorsed.
DeepSeek in dAIsy is listed for Imperial College London in an official university AI tools source. Derived availability: allowed. Derived endorsement type: officially endorsed.
Claude in dAIsy is listed for Imperial College London in an official university AI tools source. Derived availability: allowed. Derived endorsement type: officially endorsed.
GPT in dAIsy is listed for Imperial College London in an official university AI tools source. Derived availability: allowed. Derived endorsement type: officially endorsed.
Students should include a statement acknowledging their use of generative AI tools for all assessed work, specifying the tool name and version, publisher, URL, a brief description of how it was used, and confirmation that the work is their own. Further requirements such as prompts used, date of output, the output obtained, and how it was modified may also be required by individual departments.
Users of Imperial's dAIsy platform must not upload third-party content they are not permitted to share. Reuse of AI outputs must comply with licensing and academic citation norms. When communicating externally, dAIsy outputs must not be presented as Imperial's position without approval.
Imperial College London has established five Generative AI Principles (aligned with Imperial Values: Respect, Collaboration, Excellence, Integrity, Innovation) to provide a foundational framework for approaches to using generative AI in teaching, learning and assessment university-wide. The principles cover promoting critical use of AI, adopting a consistent ethical approach, and building a proactive research community around AI in education.
Unless explicitly authorised, using generative AI to create assessed work may be treated as an academic offence such as contract cheating under Imperial's Plagiarism, Academic Integrity & Exam Offences regulations. Improper use of AI can be investigated under the University's Academic Misconduct procedures.
Individual departments at Imperial may allow or prohibit the use of generative AI for specific assessments. Local (team/department/faculty) instructions take precedence over university-wide guidance. Students should check their department's current policy on using and disclosing generative AI in academic work and follow their module leader's instructions.
Imperial's dAIsy AI platform uses University SSO authentication with auditing. Prompts and metadata are logged for operational monitoring, and AI model providers are configured not to train on user data. Users' prompts and responses are not used to train external AI models. dAIsy is approved for use with unrestricted data within Imperial's secure infrastructure.
Breaches of Imperial's dAIsy Use Policy may lead to action under Academic Misconduct procedures for students and HR/disciplinary processes for staff, as well as under Information Security and Data Protection policies. Sanctions may include removal of access, grade penalties, or formal disciplinary measures.
Research at Imperial that involves people, personal data, or sensitive topics may require ethics approval, a Data Protection Impact Assessment (DPIA), and data-governance controls before using any AI tool. Researchers must verify whether their use of AI in research requires special approval, particularly when uploading private or confidential research data.
All Imperial staff and students have access to Microsoft Copilot with Commercial Data Protection when signed in using their Imperial credentials. Microsoft Copilot has no access to organizational data in the Microsoft 365 Graph. Chat results are not saved or made available to Microsoft, and data does not pass outside the organisation.
Users of Imperial's dAIsy AI platform must always apply critical judgment to AI outputs. Generative AI can produce inaccurate or biased outputs ('hallucinate'), omit context, or reflect training-data biases. Users remain accountable for the accuracy, legality, and appropriateness of any content they submit or share through the platform.
15 source attributions
official_policy_page Tracker checked at 2026年5月6日 01:00
official_guidance Tracker checked at 2026年5月6日 01:00
other Tracker checked at 2026年5月6日 01:00
other Tracker checked at 2026年5月6日 01:00
official_guidance Tracker checked at 2026年5月6日 01:00
official_guidance Tracker checked at 2026年7月15日 17:36
official_guidance Tracker checked at 2026年5月6日 01:00
official_guidance Tracker checked at 2026年5月6日 01:00
official_guidance Tracker checked at 2026年5月6日 01:00
official_guidance Tracker checked at 2026年5月6日 01:00
official_policy_page Tracker checked at 2026年5月6日 01:00
official_guidance Tracker checked at 2026年5月6日 01:00
other Tracker checked at 2026年5月6日 01:00
official_policy_page Tracker checked at 2026年5月6日 01:00
official_guidance Tracker checked at 2026年5月6日 01:00