AI 测评主要包括以下几个方面：
AI Review is a feature that allows you to review your recent changes in your codebase to catch any potential bugs.AI Review是一项功能，可让您查看代码库中的最近更改以捕获任何潜在的错误。You can click into individual review items to see the full context in the editor,and chat with the AI to get more information.您可以单击各个审阅项以查看编辑器中的完整上下文，并与AI聊天以获取详细信息。[heading3][heading3]Custom Review Instructions自定义审核说明[content]In order for AI Review to work in your favor,you can provide custom instructions for the AI to focus on.For example,if you want the AI to focus on performance-related issues,you could put:为了让AI Review对您有利，您可以为AI提供自定义说明以专注于。例如，如果您希望AI专注于与性能相关的问题，您可以输入：This way,AI Review will focus on the performance of your code when scanning through your changes.这样，AI Review在扫描更改时将专注于代码的性能。[heading3][heading3]Review Options查看选项[content]Currently,you have a several options to choose from to review:目前，您有几个选项可供选择进行审核：Review Working State查看工作状态This will review your uncommitted changes.这将查看您未提交的更改。Review Diff with Main Branch查看Diff with Main BranchThis will review the diff between your current working state and the main branch.这将查看当前工作状态与main分支之间的差异。Review Last Commit查看上次提交This will review the last commit you made.这将审查您所做的最后一次提交。
这个组呢？在群⾥如何使⽤AI的呢？Chatbot未来展望：AI辅助测评（还没做，下轮做）AI变革学情分析、作业测评、助教答疑的策略与案例作业题⽬的智能⽣成与优化AI⼯具⽣成作业题⽬AI辅助优化题⽬质量与难度主观题AI辅助批改的策略与实践基于评分量规的主观题AI批改⼈机协同批改的优化策略与实践分享基于平台数据的学情智能分析头脑⻛暴学习数据的采集与预处理利⽤AI⼯具进⾏学情挖掘与分析学情分析结果的教学应⽤场景AI助⼒作业测评的未来想象量规出来了，AI就能自动评分了•请你扮演经验丰富的教师培训师，为实习⽼师的试讲语⾔做评价。根据以下量规,请评价一位试讲⽼师的试讲录⾳⽚段:•第一步:在以下五个维度上打分(每项5分,共25分)•1.语⾔的准确性、清晰度和规范性•2.讲授的严密性和逻辑性•3.语⾔的⽣动性和趣味性•4.针对学⽣实际的可接受性和启发性•5.语⾔的艺术性•第二步:给出总分(满分25分)•第三步:以第一⼈称、700字左右,代⼊一位和蔼可亲的语⽂⽼师⻆⾊,针对试讲者的表现给出中肯评价,并提出⿎励和改进建议。改进建议请附带原⽂句⼦和具体时间。
125 What is the UK constitution?The Constitution Unit,University College London,2023.55A pro-innovation approach to AI regulation1.84.Tools for trustworthy AI like assurance techniques and technical standards can support supply chain risk management.These tools can also drive the uptake and adoption of AI by building justified trust in these systems,giving users confidence that key AI-related risks have been identified,addressed and mitigated across the supply chain.For example,by describing measures that manufacturers should take to ensure the safety of AI systems,technical standards can provide reassurance to purchasers and users of AI systems that appropriate safety-focused measures have been adopted,ultimately encouraging adoption of AI.2.85.Our evaluation of the framework will assess whether the legal responsibility for AI is effectively and fairly distributed.As we implement the framework,we will continue our extensive engagement to gather evidence from regulators,industry,academia,and civil society on its impact on different actors across the AI life cycle.This will allow us to monitor the effects of our framework on actors across the AI supply chain on an ongoing basis.We will need a particular focus on foundation models given the potential challenges they pose to life cycle accountability,especially when available as open-source.By centrally evaluating whether there are adequate measures for AI accountability,we can assess the need for further interventions into AI liability across the whole economy and AI life cycle.Consultation questions:L1.What challenges might arise when regulators apply the principles across different AI applications and systems?How could we address these challenges through our proposed AI regulatory framework?L2.1.Do you agree that the implementation of our principles through existing legal frameworks will fairly and effectively allocate legal responsibility for AI across the life cycle?L.2.2.How could it be improved,if at all?