This topic provides a solution guide for implementing AI interviews, helping companies enhance recruitment quality and efficiency.
Background
In today's fast-paced and competitive business environment, traditional interview methods are often inefficient, subjective, and slow, failing to meet modern hiring demands. AI-powered interviews streamlines candidate screening, reduces hiring cycles, and improves both efficiency and objectivity. By minimizing human bias, AI interviews enhance fairness and provide reliable, data-driven insights to improve candidate-role matching, giving companies a competitive edge in the war for talent.
Solution overview
The AI interview process consists of three main stages:
Pre-interview:
Candidate communication: Establish a clear process to inform candidates about the interview time, format, and what to expect. Provide detailed instructions on how to use the AI interview system to ensure a smooth experience.
Question bank setup: Design a targeted question bank based on the responsibilities, skills, and competencies required for each role, covering areas such as professional knowledge, work experience, problem-solving, and teamwork.
During the interview:
Audio/Video call: Select the appropriate interview format for the role and candidate.
Personalized interview: Configure the AI agent with specific parameters to provide a tailored interview for each candidate.
Anti-cheating detection: Monitor the candidate's facial expressions and actions in real time to detect potential cheating.
Post-interview:
Audio/Video archiving: Save the raw audio and video data from the interview session.
Transcript archiving: Transcribe the conversation into text and archive it for review.
Options
Interview modes
Our solution offers three interview modes. Specify the desired call type when creating your AI agent and integrate the corresponding SDK. You can experience these modes firsthand in our demo. To integrate the service, see the 添加网页链接.
Audio-only call | Vision call | Video call | |
Example |
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Interaction |
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Cost | Low | Medium | High |
Client SDKs
For detailed SDK integration instructions, see Developer guide.
SDK | Description |
Recommended
Note
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Recommended for native applications on Android or iOS. | |
Other | For development on Windows or macOS desktops, contact us by joining our DingTalk group (ID: 106730016696). |
Basic features
Personalized interviews
Alibaba Cloud provides a rich set of APIs to create a tailored interview for each candidate. You can achieve this by setting call startup parameters on the client or by configuring parameters during server-side initiation.
Setting | Description | Modifiable during call? |
LLM prompt | Pass candidate details and job information as part of the initial prompt to enable the AI to conduct a more targeted interview. | Yes |
ASR language | Set the speech recognition language (such as Chinese or English). | Yes |
TTS voice | Set the AI interviewer's voice and timbre. | Yes |
Avatar | If using a | No |
Welcome message | Set a custom welcome message for each candidate, such as, "Hello, Alice. Welcome to your interview." | No |
Conversation modes
Candidates have different speaking styles and speeds. To prevent the AI from interrupting a candidate who pauses to think, our solution offers three conversation modes:
Mode 1: Natural conversation with semantic endpointing (Recommended)
The candidate and AI engage in a natural, full-duplex conversation. When a user pauses, the semantic recognition module intelligently determines if they have finished speaking based on context. We recommend configuring a 5-second wait time (configurable) if the user is likely still speaking. The AI will respond immediately once the user has finished. For details, see Semantic endpointing.
Mode 2: Push-to-Talk
The candidate presses and holds a button to answer a question and releases it to finish. For details, see Push-to-Talk mode.
Mode 3: Natural conversation with a stop phrase
The candidate says a specific phrase (such as "I'm finished") to signal the end of their turn. You can configure multiple stop phrases for a call.
Send custom messages to clients
If you need to send custom information, such as test questions or informational cards, to the client in real-time, our platform provides a dedicated channel for this. Once received, the client can render the content or perform any custom action.
There are two ways to implement this:
Method 1: Your server can send custom messages directly to the client. See Send proactive messages to clients.
Method 2: You can embed custom commands within the LLM's response.
NoteThe custom commands can be marked with special characters, such as
{}or[]. These markers can be filtered out by the TTS node so they are not spoken aloud. Parse this content to handle custom business logic.
Pass user information to the model
When multiple candidates are being interviewed simultaneously, the LLM needs to distinguish which input comes from which user. Our platform allows you to pass custom information, such as a UserID, through to the model. See Pass through business parameters to Alibaba Cloud Model Studio.
Detect and handle user silence
You can monitor the timestamp of each user utterance by listening for the intent_recognized callback. See Agent callbacks for details. This allows you to handle cases where a user is silent for an extended period. Common actions include:
End the conversation: See StopAIAgentInstance.
Play a reminder: Have the AI play a reminder after X seconds of silence. See Vocalize notifications from AI agent.
Trigger the next question: Send a text input to the LLM to have it ask the next question. See Send text input to an LLM via API.
Conversation archiving
You can save the audio data and text transcripts generated during the entire interview session. For instructions, see Data archiving.
Advanced features
Anti-cheating system
Check item | Description | Executor |
Invalid screen detection | Detects if the screen is obscured (such as by glare, a black screen, or a white screen) for a set duration. | Real-time Conversational AI |
Number of people in the video | Provides a real-time callback with the number of people in the frame to detect extra people or if the candidate leaves. | Real-time Conversational AI |
Electronic device detection | Provides a real-time callback if electronic devices (phones, watches, headphones) are detected in the frame. | Real-time Conversational AI |
Frequent head shaking | Triggers if the candidate shakes their head twice within 5 seconds. | Real-time Conversational AI |
Frequent head nodding | Triggers if the candidate nods twice within 5 seconds. | Real-time Conversational AI |
Content overlap analysis | After the interview, you can perform plagiarism detection on the candidate's answers using an LLM to check for AI-generated content. | Your own application |
Pre-interview phone notifications
Our platform provides outbound calling capabilities, which can be used to send automated invitations before the interview and result notifications afterward.